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AI and the Future of Healthcare, Siemens Healthineers
The document presents advancements in AI and digital technology by Siemens Healthineers aimed at improving precision therapy and diagnostic imaging in healthcare. It discusses innovations such as patient twinning, automated detection of comorbidities, and patient stratification for cancer treatment. The goal is to enhance patient outcomes through individualized therapy and efficient workflows in medical settings.
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AI and the Future of Healthcare, Siemens Healthineers
- 1. Unrestricted © SiemensHealthineers, 2022 mHealth Israel Conference The Future of AI in Healthcare Joanne Grau Digitalizing Healthcare
- 2. 2 Unrestricted © SiemensHealthineers, 2022 Our purpose We pioneer breakthroughs in healthcare. For everyone. Everywhere. Unrestricted © Siemens Healthineers, 2022 2 Diagnostic Imaging
- 3. 3 Unrestricted © SiemensHealthineers, 2022 Our unique capabilities 1 Patient Twinning is currently under development. It is not for sale. Its future availability cannot be guaranteed. Patient Twinning1 Precision Therapy Digital, Data and AI Best possible description of an individual patient. Individualized therapies for the most threatening diseases. Diagnostic Imaging Imaging Market leader in diagnostic imaging Diagnostics Bringing clinical and workflow excellence to laboratories Advanced Therapies State-of-the art technology for minimally invasive procedures Varian Forging a new, more unifying, smarter standard of oncology Connects diagnosis with therapy to better guide treatment. Scales the usage of technical advances, having the next patient benefit from the knowledge generated by diagnosing and treating millions before them.
- 4. 4 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 4 01 | TODAY Workforce productivity 03 | VISION Digital twin 02 | TOMORROW Precision therapy Artificial Intelligence Unrestricted © Siemens Healthineers, 2022 4 Joanne Grau | Digitalizing Healthcare
- 5. 5 Unrestricted © SiemensHealthineers, 2022 Restricted © Siemens Healthineers, 2022 5
- 6. 6 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 6
- 7. 7 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 7 Incidental findings Automated detection of smoking related comorbidities Example: AI-Rad Companion Chest CT 1 The outcomes achieved by the Siemens Healthineers customers described here in were achieved in the customer's unique setting. Since there is no "typical" hospital and many variables exist Joanne Grau | Digitalizing Healthcare Automated detection and quantification of … Dilatation of thoracic aorta Coronary calcifications
- 8. 8 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 8 Prostate cancer severity assessment Radiologist | Artificial intelligence Example: AI-Rad Companion Prostate MR | Joanne Grau | Digitalizing Healthcare 01 02 03 04 05 Auto-segmented prostate Auto-identified lesions Calculate PI-RADS score1 Assessment & Correction Consolidated report Benefits
- 9. 9 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 9 Stroke | Automated support for emergency triage of suspected intracranial hemorrhage Courtesy of Erlangen University Hospital, Erlangen, Germany Benefits Joanne Grau | Digitalizing Healthcare Intracranial hemorrhage is one of the most devastating forms of stroke Regardless of the cause, timely and accurate diagnosis is essential for the successful care of these patients AI can automatically detect a suspected intracranial hemorrhage and alert caregivers to help prioritize critical cases
- 10. 10 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 10 01 | TODAY Workforce productivity 03 | VISION Digital twin 02 | TOMORROW Precision therapy Artificial Intelligence Unrestricted © Siemens Healthineers, 2022 10 Joanne Grau | Digitalizing Healthcare
- 11. 11 Unrestricted © SiemensHealthineers, 2022 Lung Cancer Risk prediction of lung cancer *In development. Not available for sale. Features listed are development design goals. Future availability cannot be guaranteed. Not all product offerings are available in all countries. • Using routine laboratory markers, predict the likelihood of patients developing cancer (colorectal, liver or lung) over the next 12 months • Comprehensive Metabolic Panel, Complete Blood Count and Lipid Panel • Created AI-based algorithm to predict likelihood of: • Colorectal cancer • Liver cancer • Lung cancer • Notify ordering physicians of increased cancer likelihood of a patient based on routine tests, and recommend/trigger reflex tests • Trained algorithms on ~27, 000 cases with ~100,000 encounters • Presented at AACC 2022 as WIP Combining markers from all 3 panels boost performance
- 12. 12 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 12 Lung cancer Patient stratification and selection of optimal radiation dose The concepts and information presented in this slide are based on research results that are not commercially available. SBRT = Stereotactic Body Radiation Therapy ; HER = Electronic Health Record Deep Profiler Joanne Grau | Digitalizing Healthcare Patient stratification Probability of local control after SBRT Dose prescription Can AI-based risk score help modulate therapy? Diagnosis and planning Imaging EHR Treatment parameters Tumor Information Outcome Data No stratification Deep Profiler stratification Deep Profiler reduces the local failure rate by 45% in favorable sub-group (compare with Radiomics 32% only). Help radiation oncologist on the dose escalation studies. Patient with BED=100Gy Patient with BED=180Gy Patient with BED=150Gy Lou et al, An image-based framework for individualizing radiotherapy dose, Lancet Digital Health, 2019
- 13. 13 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 13
- 14. 14 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 14 01 | TODAY Workforce productivity 03 | VISION Digital twin 02 | TOMORROW Precision therapy Artificial Intelligence Unrestricted © Siemens Healthineers, 2022 14 Joanne Grau | Digitalizing Healthcare
- 15. 15 Unrestricted © SiemensHealthineers, 2022 What if we could create a digital twin of the patient’s heart? Image courtesy of IHU Bordeaux, France |The concepts and information presented in this slide deck are based on research results that are not commercially available. Joanne Grau | Digitalizing Healthcare Multiscale, Personalized Physiological Model of the patient’s heart Similar dimensions, electrical signal activation, muscle contraction, ejection fraction, pressure dynamics Mechanistic and statistical modeling Model is under our control Potential to test and prescribe best therapy for the patient – e.g., Cardiac Resynchronization Therapy
- 16. 16 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 16 Potential to make arrhythmia therapy more patient specific 1 Lluch et al, “Is Personalized Computational Model of Atrial Fibrillation Really Personalized?“, AHA 2021 | 2 Meister et al, “Extrapolation of ventricular activation times from sparse electro anatomical data using graph convolutional neural networks”, Frontiers in Physiology-Compu- tational Physiology and Medicine 2021 | 3 Neumann et al, “A self-taught artificial agent for multi-physics computational model personaliza- tion, MIA, 2016 | The concepts and information presented in this slide deck are based on research results that are not commercially available. Joanne Grau | Digitalizing Healthcare … help identify the ablation targets that will effectively terminate persistence AF? … help identify the minimal ablation targets (catheter, RT) that will effectively terminate VT? … anticipate the effects of Cardiac resynchronization therapy on patient’s cardiac function from preoperative data? Ventricular Tachycardia Atrial Fibrillation Dyssynchrony – Heart Failure Can we …
- 17. 17 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 17
- 18. 18 Unrestricted © SiemensHealthineers, 2022 Unrestricted © Siemens Healthineers, 2022 18 01 | TODAY Workforce productivity 03 | VISION Digital twin 02 | TOMORROW Precision therapy Artificial Intelligence Unrestricted © Siemens Healthineers, 2022 18 Joanne Grau | Digitalizing Healthcare
Editor's Notes
- #3 HOOD05162003252062
- #4 HOOD05162003252381 (re-use in parts HOOD05162003241203) Our unique capabilities: Patient Twinning –personalization of diagnosis and therapy selection Precision Therapy – Intelligent and image guided treatment for the most prevalent diseases Digital, Data and AI – advance providers’ operation with tech-enabled services
- #5 Today’s artificial intelligence is available to caregivers worldwide in commercially available solutions. These solutions are primarily solutions to individual users or departments, not being connected to each other. Already at this stage, these solutions can provide significant value to healthcare providers today: Greater productivity of the individual, by automation of predictive tasks and avoidance of errors and the effort involved in their compensation Consistent results, across patients, caregivers and institutions Let me share some examples with you and for these examples I will take you into Radiology
- #7 Here an example for CT lung scans. The lungs of a patient who has suffered COVID 19. AI helps here to measure the impact of the COVID-19 disease on the lung It automatically detects and quantifies the extent of total COVID-19 abnormalities based on 3D segmentation of lesions, lungs, and lobes. During the infection – decide for admission to hospital and potentially ICU Has been used more than 350.000 times
- #8 Other than most human operators, AI scans all chest CT datasets for other incidental findings Smoking for example increases the likelihood of various severe diseases, lung cancer being of them AI automatically searches for abnormalities in chest CT datasets and can for example identify coronary calcifications or dilatation of the thoracic aorta In the event of incidental findings, the operator is notified and the corresponding report automatically pre-populated – which is a great opportunity to provide consistent, high-quality diagnosis to each patient
- #9 Another example includes MRI of the prostate to diagnose prostate cancer, which is one of the growing areas in oncology Prostate MRI is an expert task, and the growing patient numbers put additional pressure on senior radiologists Artificial intelligence reduces the workload of experienced radiologists and at the same time provides guidance to less experienced radiologists to successfully perform this expert task AI automatically segments the prostate and detects lesions. These lesions are automatically quantified and the PI-RADS score for severity assessment is calculated. The operator has the opportunity to correct the findings if needed, and AI concludes the task by populating a standardized report. A recently published study compared the findings from experienced and less experienced radiologists. The study concluded, that less experienced radiologists can provide the same level of quality as experienced radiologists, when accompanied by Artificial Intelligence. https://link.springer.com/article/10.1007/s00330-022-08978-y
- #10 This is an example from the emergency department, where time is critical One of the promising applications is running artificial intelligence on the CT scanner. In a stroke patient for example, AI can automatically detect a suspected intracranial hemorrhage and alert caregivers. This helps to prioritize these critical cases, where minutes can be decisive for the future of the patient’s life. These case can be read by radiologists in the control-room, or in their usual reading environment This concludes today’s applications and I now want to look into the near future
- #11 The next era of artificial intelligence, which is subject to current research, will greatly help to deliver more precise therapy By aggregating data along clinical pathways, Artificial Intelligence will be able to guide treatment decisions in the interest of achieving the best possible outcome and minimizing side effects
- #12 We think AI can help with this and have been collaborating with multiple healthcare institutions to try and create a predictive algorithm that could be used to help identify the potential risk a COVID-19 positive patient may have of sever illness from uncontrolled inflammation (Cytokine Storm) We’ve worked with leading healthcare institutions from Atlanta, Houston, NY and Madrid to collect and analyzer de-identified patient data and then Created AI-based algorithm to predict likelihood of: acute respiratory failure end organ failure 30 day mortality We used the data from the cohort to train the algorithms with a combined data source of ~14,500 COVID-19 diagnosed patient cases Our goal was to find the right balance of including the minimal amount of IVD test results needed to yield accurate prediction scores and ended up including Age + 9 lab different biomarkers generated within the first 3 days of hospitalization (D-Dimer, LDH, Lymph %, Eos %, CREAT, CRP, FER, INR, Troponin-I) We are now have a couple healthcare institutions performing Investigational Use Only evaluations to assess the potential clinical utility and benefit of the algorithm You can see here the current prototype being used that includes test result entry on the left side with the overall patient severity score and 3 individual risk scores for acute respiratory failure, end organ failure and 30 day mortality displayed on the top. We feel this is an important example of how AI can and will be more commonly used in the future to help create tools to predict outcomes and aid physicians in making clinical decisions.
- #13 Let’s look at an example on lung cancer treatment By taking as an input the treatment parameters, tumor information, and outcome data, we can learn the fingerprint to differentiate responder and non-responder groups. We have demonstrated that the local failure rate to radiation therapy can be reduced by 45% in the favorable sub-group and that AI can also help modulate therapy. This way, AI helps to precisely prescribe the required dose for radiation treatment for the individual patient.
- #14 Now I want to discuss and example, on what is possible when connecting information across the patient pathway – on the example of lung cancer Based on the prediction of the likelihood of patients developing cancer (colorectal, liver or lung) over the next 12 months, which is subject to current research. AI can notify the physician and the patient of the need for diagnosis, for example by chest x-ray or chest-CT. As we saw earlier, detection, highlighting and quantification of findings is commercially available today. AI also helps today in automatic contouring of organs at risk, which is required for radiation therapy. The next step in the patient pathway is treatment planning. AI-based treatment planning, which has the potential to greatly help increase the productivity of caregivers and to account for changing conditions of the patient during the radiation treatment cycle, e.g. weight loss. The latter will of course contribute to a more precise treatment to the individual patient.
- #15 Looking further into the future, our vision on how to use data and Artificial Intelligence, is to create a digital twin. A digital twin of the patient – and when we look at prevention this also includes healthy individuals.
- #16 Imaging, what if we could create a digital twin of the patient’s heart? Let us move forward and bring into the equation the physiology, the mechanistic approach of explaining how things work, in addition to statistics. Also include guidance on therapy decision, is it effective or not, predict will therapy be effective
- #17 What is one of the advantages of having a virtual patient-specific heart? Let’s look at different applications Different arrhythmia developments can be simulated on the patient. This might allow to test whether the patient will develop an arrhythmia thanks to biomarkers of the model. Moreover, once the patient has the arrhythmia, different treatment options can be simulated to have a suggestion of the optimal ablation procedure or the optimal placement or timing of a pacemaker.
- #18 Imagine – at some point in the future our data is being integrated from birth through a lifelong, physiological model that is updated with each scan and exam. And specifically designed Neural Networks are analyzing the data continuously. As a result, we don’t talk only about disease care, but we focus on health care, person centric prevention, When disease happens we have the right tools to establish the correct diagnosis and select the best treatment. For this vision many things have to evolve, specifically on the AI technology. Looking at the rapid development of computational power – in supercomputers and also at point of care – I’m optimistic that we will see the digital twin of the patient in the not-so-distant future.
- #19 Person includes both – patients and healthy people Workforce productivity Efficiency – Automation of repetitive tasks Consistency – avoidance of errors helping to reduce effort on correction and consistent high quality