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2020

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.