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​Community Acquired Pneumonia and COVID-19 Artificial Intelligence (AI) Predictive Engine (CAPE)

CAPE is an AI-enabled tool that can predict the severity of pneumonia in patients, including COVID-19 patients, based on a chest x-ray image. This Smart Health AI predictive engine enables closer monitoring and treatment of patients with severe pneumonia for improved patient outcomes through timely triaging and treatment.

CAPE was co-developed by IHiS and a multi-disciplinary team at Changi General Hospital (CGH), one of several Public Healthcare Institutions (PHIs) searching for ways to fortify their preparations and responses when COVID-19 first reached Singapore.


Why CAPE was Developed
Pneumonia is one of the leading causes of death worldwide, and the main cause of deterioration in COVID-19. The ability to quickly predict the patient’s expected severity of pneumonia enables clinicians and administrators to efficiently allocate healthcare resources and treat patients, particularly in pandemic situations, where there may be an increased need for inpatient care and critical care support.

As pneumonia severity correlates to the degree of Chest X-Ray (CXR) lung image abnormality, CGH’s Respiratory and Critical Care Medicine and Radiology teams recognized the potential in leveraging artificial intelligence to predict the severity of pneumonia from CXR images, and worked with the IHiS Health Insights team to develop CAPE.


How CAPE Works
Most of the current assessment tools in the market use either radiology images or Electronic Medical Records (EMR) data. However, CAPE is equipped with the capabilities to utilise both for a single end-to-end machine learning model. Machine learning is essentially a set of algorithms which use statistics to find patterns in massive amounts of data.


Using more than 3,000 CXR images and 200,000 data points including lab results and clinical history, CAPE was trained to generate a score based on indicators of pneumonia severity from CXR images:

a) low-risk pneumonia with anticipated short inpatient hospitalization (2 days or less)
b) the risk of mortality (death)
c) the risk of requiring critical care support (ICU)

The risk score generated by CAPE can serve as a decision support for doctors, so that patients who are likely to require critical care can be more closely monitored, and can receive treatment in a timely manner.


Initial validation tests at CGH shows that CAPE has an approximate accuracy of 80% in predicting severe pneumonia. This is comparable to traditional pneumonia severity tools that are scored manually.

To ensure ease of integration and use, CGH and IHiS incorporated CAPE into the radiology workflow as an application with only minor changes to the existing system. Ongoing work includes integrating more historical data-sets into the predictive engines, which may further improve accuracy.

Note: While CAPE has the capability to use electronic medical records (EMR) data, it has not been in use yet as more time is needed to incorporate EMA data into CAPE operationally.



Case Study

A 42-year old man who tested positive for COVID-19. CAPE predicted the need for critical care support to be at 80.6%. The patient was subsequently admitted to ICU for deteriorating oxygen levels.

(A) Frontal chest radiograph upon admission.

(B) The deep learning model heat map is overlaid the image showing pneumonia-related features. The deep-learning model risk score for the subject requiring critical care support was 80.6%.


​The Future of CAPE

Beyond local healthcare settings, CAPE can potentially be calibrated to identify and predict the severity of respiratory infections globally. This would be crucial during pandemics such as COVID-19, where there could be an increased need for inpatient and critical care support. In areas where healthcare resources may be limited, CAPE can enable prioritisation of healthcare resources so that patients who are likely to develop severe pneumonia can receive appropriate and timely care, improving patient outcomes.

The CGH team is working to validate the model in other public health institutions in Singapore to improve the robustness of the model. The team is also exploring collaborative models, including uploading it as a “freeware” collaborative tool on a research platform for interested researchers.

For more information on CAPE, read the press release.


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