- Our team analyzed clinical images, all radiographs were initially screened for quality control and all low-quality or unreadable scans were removed. A set of 5,000 high-quality chest X-ray images were analyzed for the coronavirus project. A series of 10,000 microscopic images were analyzed for heart attack research.
- By developing a ConvNET algorithm for object recognition/tracking from image data, we used machine learning techniques to predict the nature of particles.
- We developed a deep neural network (U-net) to automate image segmentation.
- Our team used various Machine Learning (ML) techniques for different classification tasks such as SVM, random forest and logistic regression, and checked for better evaluation metrics.
- Costs were reduced by lowering the numbers of required lab tests/experiments; for example, the goal of the coronavirus project was to predict based on inexpensive chest X-rays and as a result, saved the cost of expensive tests such as CT and also reduced the cost of the Patients’ length of stay in ICUs by early diagnosis (On average this model could save approximately $2,000 and 7 days of ICU stay per patient).
- Clinicians and scientists were provided with more insights to assist them in making better decisions about medical treatments and underlying conditions.
- We made case-control study predictions based on the medical images with encouraging accuracy; for example, the prediction of serious coronavirus illness based on a chest X-ray without requiring more lab tests.