Protocol

1. Title of the project:  Development and validation of Deep Learning Model to diagnose Coronary Artery Disease from Computed Tomography Coronary Angiography Images

2. Type of Study: Retrospective study

3. Aims & objectives (hypotheses if applicable): Build and validate Deep Learning Model to diagnose Coronary Artery Disease (CAD) from Computed Tomography Coronary Angiography (CTCA) Images

4. Justification for study (whether of national significance with rationale): CAD is a leading cause of mortality worldwide and in recent years there has been a trend of the younger demographic increasingly suffering from CAD, especially myocardial infarction. Diagnosis of CAD heavily relies on the use of invasive angiography in our country. Though non-invasive imaging tests like CTCA are available which can be used in a proportion of patients suspected of CAD, the lack of availability of expert radiologists/cardiologists who can report CTCA images has limited the widespread utilization of this test. Addressing this challenge necessitates the creation of advanced, real-time cardiac screening, diagnosis, and prognosis systems. Through this study, we aim to develop one such system: an accurate deep learning-based artificial intelligence (AI) model to diagnose CAD from CTCA images in a time-efficient manner. Such a model can be scaled to diagnostic centres in tier 2 and 3 cities potentially improving CAD diagnostic pathways in resource-limited settings and potentially reducing morbidity and mortality through early diagnosis. The proposal aligns with the Ayushman Bharath Digital mission to create a digital health solution ecosystem.

5. Departments involved:

Department of Computer Science and Engineering, Manipal

Department of Cardiology, Kasturba Medical College, Manipal

Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal

Cardiovascular medical imaging applications, Philips Innovation Campus Bengaluru

Department of Data Science, Prasanna School of Public Health, Manipal

6. Study period: 1 year

7. Sample size: Time-bound. All the CTCA images available in the last 10 years from hospital records and PACS will be utilized.

8. Materials and methods:  

a) Inclusion and exclusion criteria:

Inclusion criteria:

Adult patients (> 18 years of age) who have undergone CTCA in the last 10 years for diagnosis of CAD.

Exclusion Criteria:

Patients with poor quality CTCA images.

b) Biological materials required (type - blood, tissue etc and quantity): Yes ☐    No   ☒   

i) Biological material: Nil

ii) Biosafety Measures: NA

c) Statistical methods: Mean and standard deviation will be used to summarize continuous normally distributed data, and median (interquartile range) will be used to summarize non-normal data. Frequency and percentages will be used for nominal/categorical data. The steps involved in machine learning are detailed in the methods section. We will use the temporal validation method: training data will be from patients in the first 75-80% duration of the study and test data will be from the final 20-25% duration (e.g. CTCA images from the first 7-8 years of data collection period will be part of training data and the last 2-3 years will be part of test data). Using this method, we ensure that the test data is truly unseen during the training phase. After models are trained using the training data, they will be evaluated on the unseen test data against the ground truths (labels) using the confusion matrix, accuracy, f1-score, and ROC analysis.

d) Tools used: Nil

9. Detailed description of procedure/processes:

The research team from the Manipal Academy of Higher Education (MAHE) is collaborating with Indian Institute of Technology, Madras (IIT Madras) for building the AI deep learning model. A Memorandum of Understanding (MoU) will be signed to share data and expertise for effective collaboration.

After obtaining the necessary permissions and ethical clearance, CTCA images from hospital records and PACS will be accessed and copied to local storage identified for the study after appropriate de-identification of images. Relevant clinical and lab data including the CTCA reports will also be recorded.

All data, especially the image data will be de-identified, stored securely and accessed only by the research team and data scientists authorized by the research team for model building. Data will be divided into training and test data using a temporal method (as described in statistical analysis). Training data will be further divided into train and validation subsets for training the models. Appropriate data preprocessing steps will then be applied to prepare the data for ML tasks. Several candidate model architectures will be tried and the one giving the best accuracy along with good cross-validation scores (indicating robustness) will be chosen as the final model. Evaluation of the models for classification tasks will be done on unseen test data using standard methods like confusion matrix, accuracy score, f1-score, and ROC analysis.

10. Outcome measures: Accuracy of deep learning AI image classification model in diagnosing CAD.

11. Potential risks and benefits: Minimal risk. This is a retrospective study involving patient records from the past. The image data will be de-identified and stored securely so that only the research team and data scientists involved in the study have access to the data.

By developing an accurate deep learning model to diagnose CAD from non-invasive CTCA, invasive procedures like conventional angiography can be minimized or avoided. Further, real-time (few minutes) diagnostic capabilities will help to scale to secondary care settings which are resource-limited thus filling a gap in care at these places. Integration of these AI systems into clinical workflow will improve decision-making and efficiency resulting in improved patient outcomes.

12. Ethical considerations and methods to address issues:  IEC clearance will be obtained before accessing data for this study. All data, especially the image data will be de-identified, stored securely and accessed only by the research team and data scientists authorized by the research team for model building ensuring confidentiality, privacy, and security. Necessary MoU will be signed for collaboration with external teams outside MAHE (i.e., Indian Institute of Technology, Madras) for deep learning tasks and data sharing.

13. Budget (give details) and proposed funding source:

Source: AI Centers of Excellence Grant from Ministry of Education, Government of India.

This is a joint grant between MAHE and IIT, Madras. Total amount: Rs. 2 crores of which about Rs. 81.8 lakh is marked for MAHE for this project.