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2020

FDA issues face mask warning for MRI exams

After a patient’s face was burned from metal in a mask during a 3-tesla MRI scan, the U.S. Food and Drug Administration (FDA) on December 7 issued reminder guidance to patients and healthcare providers regarding wearing metal during MRI exams.

“Today, the [FDA] is informing patients and healthcare providers that patients may be injured if they wear face masks (such as surgical or nonsurgical masks and respirators) with metal parts and coatings during an MRI exam,” the agency said. “The FDA reminds patients and providers that patients should not wear any metal during an MRI.”

Because patients must wear masks to prevent the potential transmission of COVID-19, the issue has become urgent, the agency said in its statement. The patient who was injured underwent a 3-tesla MRI exam of the neck and was burned in a pattern consistent with the mask outline, the agency said.

“Given the increased use of face masks during the COVID-19 pandemic, the FDA wants patients and healthcare providers to be aware of the potential risk of face burns related to the use of patient face masks containing metal during an MRI,” the agency said. “Metal parts … [in a face mask] may become hot and burn the patient during an MRI.”

It’s appropriate for patients to wear face masks during an MRI exam due to the COVID-19 pandemic, the FDA said. But some face masks contain metal parts, like nose clips or wires, nanoparticles, or antimicrobial coating that may contain metal such as silver or copper. Before the MRI scan begins, healthcare providers who perform MRI exams should confirm the face mask has no metal.

“The metal could result in radio frequency (RF)-induced heating,” the agency said. “This may represent a hazard for MR imaging during the COVID-19 pandemic.”

The solution? Don’t allow patients to wear their own masks during an MR exam, said Tobias Gilk, senior vice president of Radiology-Planning, founder of Gilk Radiology Consultants, and an MRI safety advocate.

“The staples holding the elastic to the mask are too small to conduct heat, and since the COVID-19 pandemic, patients have been imaged in masks that have nose bridges without injury,” he said. “But antimicrobial treated fabric can heat up. So to be safe, patients should be provided with disposable surgical masks before their MRI.”

The FDA asks any patients who are burned by a face mask during an MRI scan — or providers who witness this kind of patient injury — to report the event. It also refers patients and providers to American College of Radiology (ACR) resources regarding MRI exams during the COVID-19 pandemic.

Original source:

 

COVID-19 casts long shadow on women’s imaging

As with the rest of radiology, women’s imaging in 2020 has been marked by the COVID-19 pandemic, which has resulted in drastic drops in imaging volume. At the same time, nagging questions persist about whether mammography screening exams postponed due to COVID-19 could result in higher breast cancer rates later on.

But researchers and readers have also discovered hopeful stories in women’s imaging in 2020, including improvements in MRI and an assurance in the benefits of breast screening programs.

Before the pandemic swept the news cycle, 2020 was supposed to be a big year for breast density notification laws, as there has been continued discussion about implementing federal notification regulations and whether state laws reliably improve access to supplemental screening.

Instead, researchers and readers appear to have doubled down on questioning established principles. With norms and expectations thrown out of whack, it’s no wonder we’re reevaluating what used to be day-to-day practices.

For breast imagers, this has led to questioning whether screening mammography still makes sense (it does) or if there’s a way to improve MRI for breast imaging (there is). However, questions around breast screening and the use of MRI pale in comparison with the paramount topic of the year: the COVID-19 pandemic.

Women’s imaging upended by the pandemic

It’s hard to overstate the effects of COVID-19 on the entire continuum of care for women’s imaging in 2020. From screening to diagnosis to breast cancer treatment, facilities have learned to cope with massive declines in imaging volume and contend with providing quality care while keeping patients and staff safe.

An April survey from market research firm IMV Medical Information Division, a sister company of AuntMinnie.com, showed just how much of an impact the pandemic has had on women’s imaging from the start. X-ray mammography units experienced an astounding 70.4% decline in procedure volume early in the year — a drop much higher than the second-most affected modality, fixed radiographic fluoroscopy systems, which experienced a 48.1% decline in volume.

Impact on procedure volume due to novel coronavirus in U.S.
Imaging modality Procedure volume
Mobile general x-ray units -6.8%
Fixed PET and/or PET/CT -24.5%
CT scanners -37.5%
Fixed general x-ray radiography units -38.6%
Fixed C-arm systems -40.1%
Ultrasound units -42.7%
Portable C-arm units -44.6%
Fixed nuclear medicine cameras -47%
Fixed MRI scanners -47.4%
Fixed radiographic fluoroscopy systems -48.1%
X-ray mammography units -70.4%

The pandemic also resulted in breast imaging facilities getting creative to keep things running as smoothly as possible. In April, breast imaging chairs from across the U.S. shared how they’re triaging patients and managing workflows in an uncertain time.

The changes implemented included temporarily stopping routine mammography and ultrasound screenings — a factor that likely contributed to the reported drop in procedure volume. The institutions also adopted stricter infection control policies, such as implementing social distancing in waiting areas, requiring staff and patients to wear masks, and limiting the number of appointments per day.

As the pandemic has continued, women’s imaging leaders are now asking new questions. Namely, how will delays in screening and treatment ultimately affect breast cancer outcomes? It’s possible some of the discussion at RSNA 2020 will focus on this very topic.

It’s still too early to give a definitive answer on the long-term effects of the pandemic for patients or facilities. However, even back in April, Dr. Jennifer Harvey from the University of Rochester Medical Center was optimistic that at least some of the key learnings will be positive.

“I’m hopeful this crisis will build collaboration between our subspecialty divisions and our community and regional sites,” she wrote. “We can all be stronger for going through this together.”

Big questions around screening mammography

Screening mammography may be a half-century old, but it has been a surprisingly hot women’s imaging topic this year. Readers and researchers want to know whether programs designed to reduce late-stage breast cancer diagnoses are still beneficial in 2020. The answer appears to be a resounding yes.

Mammography screening reduced the risk of dying of breast cancer by 41% within 10 years of diagnosis, according to the findings of one May study that included nearly one-third of the screening-eligible population in Sweden. The study also found a 25% reduction in advanced breast cancer incidence among women who used screening mammography compared with those who did not.

One of the researchers behind that study was Dr. László Tabár, a Swedish radiologist and oncologist known worldwide as the “father” of mammography screening. In a well-received interview with AuntMinnie.com, Tabár said studies like the one above are important for reassuring physicians that screening is the best way to reduce the risk of premature death from breast cancer.

“Breast cancer is a terrible disease and it kills too many women, but by carrying out these very demanding and complex studies, it has been proved that early detection combined with efficient treatment can significantly reduce mortality from the disease,” he said.

Despite the consensus that breast screening saves lives, debate still swirls regarding the ideal ages and frequency with which to conduct routine mammography. This is partially because breast cancer screening programs are expensive to run, especially in the U.S.

A JAMA Internal Medicine study found that the total cost of annual breast cancer screening for commercially insured women younger than age 50 was $2.1 billion — an astounding sum that made the research team question the cost-effectiveness of screening younger women.

“These costs are borne despite the unclear trade-off between clinical benefits and risks of screening women aged 40 through 49 years,” the authors wrote in the article.

Annual breast cancer screening among women 40-49 with commercial insurance
Exams Mean cost per beneficiary screened Total national cost
Screening mammography $249 $1.5 billion
Recall $56 $337 million
Other diagnostic tests $45 $273 million
Total screening and evaluation $353 $2.1 billion

In a similar study, a Harvard-led research team calculated whether it is cost-effective to perform screening mammography in healthy older women. In the resulting Annals of Internal Medicine study, the team found breast cancer deaths were not substantially reduced for Medicare beneficiaries older than 75 who continued annual mammography, compared with those who did not.

But in a critique of that study, clinicians at Johns Hopkins said the research didn’t include data of women screened with newer and better screening technologies, including digital breast tomosynthesis (DBT). Like its technological predecessors, DBT allows clinicians to detect cancer early. But the novel ability made possible by DBT to view slides in three dimensions has helped breast imagers in 2020 find more cancers with fewer recalls.

DBT’s strength for cancer detection was perhaps most apparent in a February Journal of the American College of Radiology study that found the screening modality identified more than twice as many cancers as full-field digital mammography (FFDM) alone or as a combination of DBT and FFDM.

Hopeful promise of MRI for breast imaging

As the year went on, and perhaps as news of the COVID-19 pandemic grew dispiriting, women’s imagers turned their attention to a topic a little more hopeful — MRI for breast imaging. While the use of MRI for screening and diagnostic imaging has been a popular topic for most of the year, MRI news really picked up in the summer and fall.

In August, researchers detailed some of the advances making MRI quicker, more accurate, and less costly for breast screening at the Society for MR Radiographers & Technologists annual meeting. One of the more promising advancements discussed was the use of abbreviated protocols that can cut down scan time to mere minutes.

In fact, one study showed a 10-minute MRI scan found more than twice the number of invasive cancers in women with dense tissue than DBT. The same research team is now investigating whether abbreviated MRI screening could be cost-effective and still accurate if conducted every two to three years.

Another big MRI topic in 2020 is the use of diffusion-weighted imaging (DWI), a way to perform contrast-free MRI. Getting rid of gadolinium could be a huge win for women’s imaging, since it would not only cut down on appointment time but also reduce concerns regarding the contrast agent’s safety.

The future of DWI-MRI for breast imaging may not be far off, either. In May, researchers detailed how they repurposed a DWI-MRI protocol originally designed for brain imaging. The multiplexed sensitivity-encoding (MUSE) protocol resulted in better image quality and improved signal-to-noise ratio for breast lesions.

 

In 2020, researchers also demonstrated the potential of MRI to improve breast cancer care. A March study found patients with breast cancer who underwent presurgical MRI had fewer positive surgical margins and repeat surgeries than those who did not. The study highlighted that MRI can have big breast imaging benefits — even with longer scan times and the need for gadolinium.

All of these studies point to MRI being a surprisingly bright spot in an otherwise dark year for women’s imaging. It’s almost certain that researchers at RSNA 2020 will continue to share advances making MRI even better, giving breast images another tool in their arsenal to identify cancer early and ultimately save lives.

By Theresa Pablos, AuntMinnie staff writer

Copyright © 2020 AuntMinnie.com

Moderna says its covid-19 vaccine is nearly 95% effective

The news comes hot on the heels of an announcement last week from Pfizer, which reported that its own covid-19 vaccine was more than 90% effective.

More good news: US drug company Moderna announced today that early trials of its covid-19 vaccine show that it is 94.5% effective. The news comes hot on the heels of a similar announcement last week from Pfizer, which reported that its own covid-19 vaccine was more than 90% effective. With covid-19 having killed 1.3 million people worldwide—more than 245,000 in the US alone—these results bring a glimmer of hope amid the gloom.

How it works: Like Pfizer, Moderna is developing an RNA vaccine. These work by injecting a piece of genetic material into a person’s body that contains instructions for how to create the spike protein, the signature mechanism the coronavirus uses to invade its victim’s cells. Once the vaccine is injected, a person’s body will use those instructions to create its own version of the spike protein. When the immune system spots these proteins, it mounts defenses against them that will also repel real viral intruders in the future.

Numbers game: Given the global crisis, both companies are hoping that the FDA will rush through its approval process. But before this happens, independent number crunchers will need to look at the results again. Pfizer’s 90% score is based on a trial of more than 40,000 in which 85 out of 94 people who got sick had not been vaccinated. Moderna’s score comes from a trial of more than 30,000 in which 90 out of 95 people who got sick had not been vaccinated. Moderna also reported that all 11 severe cases in its trial were in the non-vaccinated group; Pfizer has not released equivalent figures.

Both companies acknowledge that the results might change as more people in the trials get sick. We also do not yet know how long immunity will last or if the vaccines stop people from spreading the virus as well as preventing symptoms. Despite these caveats, the results have exceeded expectations. “I had been saying I would be satisfied with a 75 percent effective vaccine,” Anthony Fauci told the New York Times. “Aspirationally, you would like to see 90, 95 percent, but I wasn’t expecting it. I thought we’d be good, but 94.5 percent is very impressive.”

Mass production: Moderna says that it will be able to produce 20 million doses—earmarked for the US—by the end of the year. Pfizer is making 50 million doses available worldwide in the same time frame.

Not over yet: These quantities may sound big, but we will need many billions of doses before vaccines can beat back the virus on a global scale. Manufacturing and distributing these vaccines would be a vast undertaking at the best of times, let alone when the world’s economies and supply chainsare already reeling from the pandemic. RNA vaccines need to be kept cold: Pfizer’s needs to be kept at -94 °F, though Moderna’s, which seems to be stable at -4 °F, can be kept up to a month in a normal fridge. Both vaccines also require two shots taken a few weeks apart to work.

Given these obstacles, having two vaccines in the running, and two companies ready to manufacture them, makes the future look that much brighter.

by Will Douglas Heavenarchive page

Copyright MIT Technology Review

CMS STEPS UP WITH SUPPORT FOR NEW AI

Stakeholders consider the move important as new devices and treatments generally aren’t widely used until the US government authorizes payments for Medicare and Medicaid patients.

New technology doesn’t really begin to change an industry landscape until someone is willing to pay for it, and AI in healthcare is no exception.

To wit, a recent article at Wired notes that the US Centers for Medicare & Medicaid Services (CMS) “recently said it would pay for use of two AI systems: one that can diagnose a complication of diabetes that causes blindness, and another that alerts a specialist when a brain scan suggests a patient has suffered a stroke.”

While that’s of obvious significance for Medicare and Medicaid patients, the article also points to the importance the move could have for the systems’ wider use.

“(N)ew devices and treatments generally aren’t widely used until the US government authorizes payments for Medicare and Medicaid patients,” the article explains, adding that “(p)rivate insurers often take their cues on whether to cover a new invention from CMS, although they usually pay higher rates.”

One of the systems, called ContaCT, from San Francisco startup Viz.ai, “is installed in a hospital emergency department to alert a neurosurgeon when algorithms see evidence on a CT scan that a patient has a blood clot in their brain.”

The other system consists of software called IDx-DR, created by Digital Diagnostics of Oakdale, Iowa, which analyzes photos of a person’s retinas to diagnose diabetic retinopathy, a complication of diabetes that can cause blindness.

“This is very important for everyone in AI,” said ophthalmologist Michael Abramoff, the CEO of Digital Diagnostics. The proposal to pay for IDx would also cover other AI tools that diagnose diabetic retinopathy.

While the two cases are encouraging for developers aiming to get their new AI tools approved for payment by CMS, they also demonstrate the myriad complexities that come into play as policymakers which new systems are worth supporting.

According to the article, the agency determined that the ContaCt software was worth supporting because of evidence that if significantly improves stroke treatment, while “IDx was the first AI product approved to diagnose disease, a clinical call previously made only by human physicians.”

“Diabetes is an epidemic, and diabetic retinopathy is a leading cause of blindness,” noted one provider who is already using IDx with patients. “Hopefully, new reimbursement models will enable this technology to propagate much faster.”

Copyrights: https://www.healthcareitnews.com/ai-powered-healthcare/cms-steps-support-new-ai

Siemens aporta su inteligencia y conectividad para producir miles de respiradores

  • La multinacional alemana colabora con GPAInnova e Industria para salvar cientos de vidas
  • El ventilador de emergencia ya está plenamente homologado por Sanidad
  • El departamento de Reyes Maroto actúa de impulsor de proyectos publico-privados

 

Cada gran proyecto industrial que estos días recibe las homologaciones de las autoridades sanitarias para combatir el coronavirus ha contado con la colaboración de organismos públicos y privados. Es el caso del dispositivo de ventilación de emergencia ‘Respira’, creado en tiempo récord por la multinacional catalana GPAInnova y en el que ha participado de forma relevante la compañía Siemens Digital Industries, en calidad de socio tecnológico, así como el Ministerio de Industria, Comercio y Turismo, en su tarea de impulso, ayuda y seguimiento en todo el desarrollo.

El nuevo dispositivo sanitario, creado prácticamente sobre la marcha gracias a la capacidad, talento, reflejos y compromiso de todas las empresas implicadas, acaba de obtener la autorización de la Agencia Española de Medicamentos y Productos Sanitarios (AEMPS), responsable de garantizar a la sociedad -desde la perspectiva de servicio público-, la calidad, seguridad, eficacia y correcta información de los medicamentos y productos sanitarios, desde su investigación hasta su utilización.

El prodigio técnico responderá desde las próximas horas a la emergencia sanitaria mundial para aliviar una demanda que resulta esencial para salvar la vida de los afectados más graves por la COVID-19. A grandes rasgos, el prototipo ya homologado automatiza los resucitadores manuales tipo AMBU (los más frecuentes del mercado), asistiéndolos de manera automática y monitorizada. A partir de ahora, la maquinaria de producción no descansará día y noche para proporcionar al sistema sanitario entre 150 y 200 respiradores diarios.

La contribución de Siemens se ha centrado en la parte electrónica del equipo, incluida la pantalla táctil de visualización y control Simatic HMI y con todas las fuentes de alimentación, entre otros elementos. De esta forma, el gigante alemán aporta la inteligencia y conectividad, mientras que SMC incorpora los pulsadores de alta precisión y TEG colabora con el cableado y montaje eléctrico en la fábrica de Vilablareix (Girona) y MAM con la producción de carcasas, el montaje final y calibrado en la fábrica de Santa Perpetua de Mogoda (Barcelona). Por su parte, GPAInnova -alma máter del proyecto- que acomete toda la ingeniería, conocimiento y maquinaria de acabado superficial de metales.

En el origen del proyecto destaca el papel impulsor del ministerio que lidera Reyes Maroto que para contactar con decenas de empresas para promover la reconversión de muchas actividades empresariales en favor de las necesidades más acuciantes del país. En el caso del dispositivo ‘Respira’, el proyecto “ha contado desde el primer momento con el apoyo de la secretaría General de Industria y Pyme del Ministerio de Industria, Comercio y Turismo, que ha hecho un seguimiento constante de su desarrollo, acompañando y asesorando a GPAInnova, como de la Agencia Española del Medicamento y Productos Sanitarios, que ha tramitado la autorización en tiempo récord”.

Según informan fuentes de GPAInnova, el dispositivo Respira, a diferencia de otros prototipos, “permite la variación del volumen insuflado, frecuencia y la relación inspiración y expiración electrónicamente y tiene unas altas prestaciones que permiten monitorizar las variables de los pacientes de forma personalizada y controlarlos de forma remota para facilitar su gestión en los hospitales”. Asimismo, el equipo “incluye la electrónica necesaria para controlar y monitorizar en tiempo real y de forma remota variables como la frecuencia, el volumen, presión y caudal de aire aportados al paciente mediante tecnología aportada por Siemens Digital Industries y a través de un aparato de impulsión de altas prestaciones fabricado por SMC,además de TEG y MAM”.

Otras de las ventajas del respirador son su diseño robusto y transportables, especialmente valioso cuando están llamados a desplazarse tanto a hospitales establecidos como de emergencia, así como la capacidad de monitorización de múltiples dispositivos conectados. Gracias a este última innovación, el personal médico requerido para la supervisión del dispositivo se reduce drásticamente pudiendo centrarse en las actuaciones prioritarias.

GPAInnova calcula que inicialmente se van a poder producir entre 150 y 200 unidades diarias, hasta 1000 unidades semanales y progresivamente aumentará la producción hasta las 300 unidades diarias. Para hacerlo posible, ya se han realizado las pruebas de producción de la cadena de montaje para la realización de los dispositivos usados en el estudio clínico con pacientes afectados por COVID-19. Entre los receptores inmediatos de los equipos se encuentran el Hospital Clínic de Barcelona y el Centre de Medicina Comparativa i Bioimatge del Institut de Recerca Germans Trias i Pujol – Can Ruti de Badalona y el Hospital de Sant Joan de Déu de Barcelona.

AI COVID-19 An algorithm developed in a collaborative approach

The coronavirus pandemic continues to spread, causing unprecedented system disruption and confronting healthcare professionals around the world with clinical and operational challenges. We believe the COVID-19 pandemic will significantly change healthcare systems. Specifically, it will serve as a catalyst for the development of more effective, efficient and, above all, more humane medicine.

Coronaviruses (CoV) are a large family of viruses that cause diseases ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV). COVID-19 is caused by the severe-acute respiratory syndrome Coronavirus 2 (SARS-Cov2) and has a high case fatality rate of up to 4% (1). Due to a high reproduction number and the infectious nature of the disease, tools for rapid testing and evaluation are vital to track and mitigate the spread.

At Siemens Healthineers we understand the urgency and complexity of this situation. In a collaborative approach together with our research network and collaboration partners, we have developed the CT Pneumonia Analysis2 algorithm.

COVID-19: A challenge in diagnosis and therapy planning

COVID-19 is confirmed via a reverse transcription polymerase chain reaction (RT-PCR)3, a laboratory technique to detect genetic material from any pathogen, including a virus. However, the sensitivity of this test may be as low as 60-70%4 and can cause false negatives in the first few days, which means in the best case patients are still treated as if they have the virus, while in the worst case infected patients might be treated as not having the virus, thereby posing a potential risk to other patients. Moreover, a current a shortage of available testing kits is contributing to undetected spread.

Although the role of CT and X-ray for diagnosis is currently being debated, two preliminary studies5 showed that Chest CT imaging of the lung provides improved sensitivity when compared to RT-PCR for patients suspected of having COVID-19. The primary features seen on a lung affected by COVID-19 are peripheral focal or multi-focal ground glass opacities, consolidation and crazy-paving patterns.

Opacity lesions in the peripheral and posterior lungs on CT images are indicative of COVID-19 pneumonia. CT can play an important role in the evaluation of COVID-19 providing advanced imaging evidence

Non-contrast chest Computed Tomography has been useful not only to detect, quantify severity, and assess progression of the disease, but also to support the design of a curative therapy. Similar results can be achieved with Chest X-rays, which can be useful in tracking the disease progression and follow-up.

A collaboration as an answer to the complex situation

Our collaboration partners and our own expert teams below are aware of the urgency and complexity of the current situation. That’s why we decided to jointly focus on the development of our new algorithms to face the COVID-19 pandemic together. Our aspiration to drive innovations forward so people live healthier and longer lives is more valid today than ever before.

(1) Hôpital Foch, Paris, France

(2) Northwell Health, New York, NY, USA

(3) University Hospital Basel, Clinic of Radiology & Nuclear Medicine, Basel, Switzerland

(4) Vancouver General Hospital, Vancouver, Canada

And with our unique AI research and development group in Princeton, our software development center in Bangalore, our product experts of CT in Forchheim, our customer collaboration partners in Paris and our own supercomputer we were able to enhance our AI portfolio with this algorithm, which works on CT images. We are stepping up as a partner to support healthcare systems helping them to deliver high-value care to patients and families and developing with us together the CT pneumonia analysis.

(5) Siemens Healthineers, Bangalore, India Software Development Center

(6) Siemens Healthineers, Forchheim, Germany CT chest Product Development and Manufacturing

(7) Siemens Healthineers, Princeton, NJ, USA Digital Technology and Innovation

(8) Siemens Healthineers, Paris, France AI collaborations

 

How could AI be beneficial for radiology in the context of COVID-19?

To improve COVID-19 therapy, there is an urgent need to increase global knowledge about this new coronavirus. With the inclusion of radiological findings in confirming COVID-19 diagnosis the workload of radiologists has increased. AI-powered analysis of chest scans has the potential to reduce this growing burden on radiologists, who must review and prioritize a rising number of patient chest scans.

Human efficiency and accuracy are compromised as a result of the overwhelming workload. The automation aspect of AI can offer support. AI can be a powerful aid in recognizing the lesions in CT images and even quantitatively characterizing the findings and comparing changes between exams, which is crucial for precision medicine. AI may support clinicians to answer several questions such as patient triaging, diagnosis (in combination with RT-PCR tests and epidemiological risk), assessment of severity and progression, and response to therapy in patients exhibiting COVID-19 symptoms.

 

How does the CT Pneumonia Analysis2 work?

The algorithm is designed to automatically identify and quantify abnormal patterns in the lungs, enabling simple to use analysis of non-contrasted chest CT scans for research purposes. The system identifies lungs, lobes and abnormalities associated with pneumonia. It also computes high opacity abnormalities as they were shown to correlate with severe symptoms. The results could be used to analyze the severity and the progression of abnormalities in patients exhibiting COVID-19 symptoms. First, the abnormal patterns in the lungs are automatically segmented in 3D.

Second, disease severity measures are computed: two global and two lobe-wise measures.

Percentage of Opacity (POO): percentage of predicted volume of abnormalities compared to the total lung volume.

Percentage of High Opacity (POHO): percentage of predicted high opacity volume compared to the predicted volume of abnormalities

Lung Severity Score (LSS): measures the extent of abnormalities across each lobe

Lung High Opacity Score (LHOS): measures the extent of abnormalities with high opacity for each lobe

The POO and LSS quantify the extent of lung involvement and the distribution of involvement across lobes, respectively. Given that high opacity abnormalities (i.e., consolidations) were shown to correlate with severe symptoms, two new severity measures POHO and LHOS were introduced to complement the assessment. They quantify the extent of high opacity abnormalities and their distribution of involvement across lobes, respectively. The performance of the method in estimating POO, LSS, POHO, and LHOS is evaluated on a database of 100 COVID-19 cases and 100 controls from multiple institutions from Canada, Europe, and the U.S. Ground truth is established by computing the same measures from manual annotations of the lesions, lungs, and lobes.

 

Learn more about system requirements and apply the COVID-19 CT pneumonia analysis in your research environment for free.

To make it as easy as possible for you to use our new algorithm, we provide it to you via all our reading and interpretation solutions you use already in your existing workflows.

 

syngo.via openApps CT pneumonia analysis: With OpenApps, syngo.via connects you to the Siemens  Helathineers Digital Marketplace and its innovative offerings. You can benefit from the integrated solution that is your new way to organize and discover apps from Siemens Healthineers and other vendors – directly on your syngo.via. Simply click on the shopping cart icon in the upper-right corner of your syngo.via VB30A or higher. A Frontier License is not needed to access the CT Pneumonia Analysis prototype.

syngo.via Frontier CT pneumonia analysis: synio.via Frontier allows you to strengthen your clinical opinion leadership with easy access to numerous post-processing prototypes that are seamlessly integrated with your routine syngo.via environment. The new CT Pneumonia Analysis2 prototype is included in the dedicated syngo.via Frontier Prototype Store, which is continuously enriched with new contributions.

AI-Rad Companion Research CT pneumonia2 analysis: The AI-RAD Companion, our family of AI-powered augmented workflow solutions, running on the teamplay digital health platform, helps to reduce the burden of basic repetitive tasks and may increase your diagnostic precision when interpreting medical images. Its solutions provide automatic post-processing of imaging datasets through AI-powered algorithms. The automation of routine workflows with repetitive tasks and high case volumes helps you to ease your daily workflow – so that you can focus on more critical issues.

 

Beyond research, AI algorithms may indicate signs of COVID-19 on chest X-rays embedded in the clinical workflow.

Artificial Intelligence plays an important role today and its support can contribute to radiologist’s work. The AI-Rad Companion Chest X-ray6, a new part of the AI-Rad Companion product family is another solution where deep learning is embedded in a dedicated software. The AI-Rad Companion Chest X-ray automatically characterizes radiographic findings for a lung X-ray (Upright patient position and PA direction). It works like a second or third reader to support radiologists in differential diagnosis and clinical decision-making. It is capable of characterizing pneumothorax, pulmonary lesions (nodules, masses, and granuloma), atelectasis, consolidation, and pleural effusion. Atelectasis and consolidations are findings that show a correlation with signs of pneumonia that is caused by the SARS-CoV-2 virus.

 

1Wilson N, Kvalsvig A, Barnard LT, Baker MG. Case-Fatality Risk Estimates for COVID-19 Calculated by Using a Lag Time for Fatality. Emerg Infect Dis. 2020;26(6).

2For Research Use Only. Not for use in diagnostic procedures.

3CDC. CDC Clinical Criteria [Internet]. 2020. Available from: https://www.cdc.gov/coronavirus/2019-nCoV/hcp/clinical-criteria.html, 20.04.2020.

4Kanne JP, Little BP, Chung JH, Elicker BM, Ketai LH. Essentials for radiologists on COVID-19: an update—radiology scientific expert panel. Radiology. 2020;200527.

5https://pubs.rsna.org/doi/10.1148/radiol.2020201365, 22.04.2020

6AI-Rad Companion Chest X-ray is currently not for sales in the United States and other selected countries

ACR Recommendations for the use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection

UPDATED MARCH 22, 2020

As COVID-19 spreads in the U.S., there is growing interest in the role and appropriateness of chest radiographs (CXR) and computed tomography (CT) for the screening, diagnosis and management of patients with suspected or known COVID-19 infection. Contributing to this interest are limited availability of viral testing kits to date, concern for test sensitivity from earlier reports in China, and the growing number of publications describing the CXR and CT appearance in the setting of known or suspected COVID-19 infection.

To date, most of the radiologic data comes from China. Some studies suggest that chest CT in particular may be positive in the setting of a negative test. We want to emphasize that knowledge of this new condition is rapidly evolving, and not all of the published and publicly available information is complete or up-to-date.

Key goals for the U.S. health care system in response to the COVID-19 outbreak are to reduce morbidity and mortality, minimize disease transmission, protect health care personnel, and preserve health care system functioning.

The ACR believes that the following factors should be considered regarding the use of imaging for suspected or known COVID-19 infection:

  • The Centers for Disease Control (CDC) does not currently recommend CXR or CT to diagnose COVID-19. Viral testing remains the only specific method of diagnosis. Confirmation with the viral test is required, even if radiologic findings are suggestive of COVID-19 on CXR or CT.
  • For the initial diagnostic testing for suspected COVID-19 infection, the CDC recommends collecting and testing specimens from the upper respiratory tract (nasopharyngeal AND oropharyngeal swabs) or from the lower respiratory tract when available for viral testing.
  • Generally, the findings on chest imaging in COVID-19 are not specific, and overlap with other infections, including influenza, H1N1, SARS and MERS. Being in the midst of the current flu season with a much higher prevalence of influenza in the U.S. than COVID-19, further limits the specificity of CT.

Additionally, there are issues related to infection control in health care facilities, including the use of imaging equipment:

  • Primary care and other medical providers are attempting to limit visits of patients with suspected influenza or COVID-19 to health care facilities, to minimize the risk of spreading infection. The CDC has also asked that patients and visitors to health care facilities be screened for symptoms of acute respiratory illness, be asked to wear a surgical mask and be evaluated in a private room with the door closed.
  • In addition to environmental cleaning and decontamination of rooms occupied by a patient with suspected or known COVID-19 infection by thorough cleaning of surfaces by someone wearing proper protective equipment, air-flow within fixed radiography or CT scanner rooms should be considered before imaging the next patient. Ventilation is an important consideration for the control of airborne transmission in health care facilities . Depending on the air exchange rates, rooms may need to be unavailable for approximately 1 hour after imaging infected patients; air circulation rooms can be tested.
  • These measures to eliminate contamination for subsequent patients may reduce access to imaging suites, leading potentially to substantial problems for patient care.

Based on these concerns, the ACR recommends:

  • CT should not be used to screen for or as a first-line test to diagnose COVID-19
  • CT should be used sparingly and reserved for hospitalized, symptomatic patients with specific clinical indications for CT. Appropriate infection control procedures should be followed before scanning subsequent patients.
  • Facilities may consider deploying portable radiography units in ambulatory care facilities for use when CXRs are considered medically necessary. The surfaces of these machines can be easily cleaned, avoiding the need to bring patients into radiography rooms.
  • Radiologists should familiarize themselves with the CT appearance of COVID-19 infection in order to be able to identify findings consistent with infection in patients imaged for other reasons.

  • (Updated March 22, 2020) As an interim measure, until more widespread COVID-19 testing is available, some medical practices are requesting chest CT to inform decisions on whether to test a patient for COVID-19, admit a patient or provide other treatment. The ACR strongly urges caution in taking this approach. A normal chest CT does not mean a person does not have COVID-19 infection – and an abnormal CT is not specific for COVID-19 diagnosis. A normal CT should not dissuade a patient from being quarantined or provided other clinically indicated treatment when otherwise medically appropriate. Clearly, locally constrained resources may be a factor in such decision making.

Recommended Resources:

Centers for Disease Control:

Radiologic articles and collections:

How Artificial Intelligence Might Personalize Healthcare

You won’t find many doctors who went to medical school to become data clerks. But that’s what many have become. Sometimes with their backs to patients, they sit in front of a computer screen, typing and clicking. Some may try to carry on conversations with their patients as they input data. But it’s tough. Technology has gotten in the way of the doctor-patient bond. Artificial intelligence might help get rid of it.

Where Natural Language Processing (NLP) Fits

Smart algorithms – fueled with natural language processing – are being groomed to recognize doctors’ questions and patients’ answers; extract key points and put the information into electronic health records. Doing so might save doctors between a third and half of their workdays, possibly reducing burnout and giving doctors more time with patients.

The key word, of course, is “might.”

The future of medical AI is highly speculative. But in some cases, the speculation may be warranted.
Much depends on the development of NLP, an enabler of medical artificial intelligence (AI). For years it has been known that algorithms capable of processing written language can extract laboratory test data, even from unstructured clinical notes. Similar potential exists in the doctor’s office.

I have seen an NLP-based system that listens to doctor-patient exchanges; translates what was said into text; extracts key words and phrases; then plugs them into a structured medical report. Strategically placed microphones pick up the voices of doctor and patient; smart algorithms do the rest.
This barely hints at how NLP might change health care for the better. In late April 2019, researchers described their development of an NLP-based system that translates human thoughts into a synthetic voice. In this research, described in a paper published April 24 in the journal Nature, an electronic sensor implanted onto a human brain picks up signals meant to produce speech. An AI algorithm translates these signals into a synthetic voice.

If commercialized, the technology might give voice to patients who cannot speak for themselves – victims of stroke, traumatic brain injury or neurodegenerative diseases, such as Parkinson’s, multiple sclerosis or amyotrophic lateral sclerosis (Lou Gehrig’s disease). But such technology could be a long time away. Maybe a decade or more. A lot of development needs to be done.

In the nearer future, smart algorithms might help patients make sense of what they access through patient portals.Greg Freiherr

Why AI Might Soon Improve Healthcare

In the nearer future, smart algorithms might help patients make sense of what they access through patient portals – conduits for transferring medical information to patients. The potential of NLP to do this has already been demonstrated. So has the potential to make technical explanations understandable by the public.

A “chatbot” called Bold360ai is being sold to businesses to interpret complex language for their customers. As Bold360ai reportedly holds textual conversations, it “remembers” context. Is it farfetched, therefore, to believe that similarly chatty AI algorithms might interpret medical language in context?
Chatbots like this could have an enormous effect. Imagine doctors concentrating just on patients’ most complex questions; and patients thoroughly understanding their medical information.

And NLP might be extended even further – to patient questionnaires, like the ones that patients now fill out routinely in waiting rooms.

Individualizing Health Care

What if smart algorithms turned the data in patient questionnaires into tailor-made healthcare strategies? A screening mammography strategy, individualized for a specific woman, might be based on all the risk factors that determine her vulnerability to breast cancer. Age, currently the sole basis for mammography screening guidelines, might be considered along with family history. Smart algorithms might pluck breast density and past biopsy results from the patient’s electronic record; calculate the benefit/harm ratio for a specific patient to have a screening mammogram annually or biannually; and put the findings into a strategy for the doctor and patient to discuss.

What if similar strategies could be developed for patients at risk of developing lung cancer? Diabetes? Heart disease? Already a company is developing an NLP text mining platform that digs into medical risk factors, monitors patients for these risks, measures quality of care, even improves patients’ clinical documentation.

Taking this a step further, could AI provide the scientific basis for factors whose role in disease now is only suspected? Among them: “food insecurity” – how many times have we heard news reports about people who must choose between medication and food? – mental health and substance abuse.

Helping Doctors Get Back To Their Roots

In the meantime, AI might be built into “virtual medical assistants,” processing data about patient interactions in the context of medical literature to help physicians apply clinical guidelines; monitor the quality of care they give; predict adverse drug events; even identify rare diseases.

If these possibilities turn into realities, physicians might be able to do what led them to medical school in the first place.