07. Exercise 1: Solution

Good work. In this exercise you’ve practiced some crucial skills you’ll use often - how to choose a clinical problem, researching basic facts about the disease, and framing the clinical problem as a machine learning task.
There is no single right answer to the exercise, here is a sample solution below.

Lesson 1 Exercise – Sample Solution

Part 1: Choosing a clinical case.

I chose to focus on the case - COVID-19 Compatible Chest CT Pattern.

Part 2: Background research.

I then focused on Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT paper by Bai, H.X., Hsieh, B., et. al. for my background research.

Part 3: Framing the problem as a machine learning task.

Per the ACR-DSI, the assigned task is to provide a likelihood of a diagnosis compatible with COVID-19 using chest CT data. I believe an initial step in solving this clinical problem, determining whether any abnormal findings are present on a chest CT, would be well-framed as an object detection task. The output would be a bounding box delineating the areas of abnormality that could indicate viral pneumonia. Based on recent literature, such findings could include consolidation, bilateral and peripheral disease, linear opacities, “crazy-paving” patterns, and the “reverse halo” sign. While these findings are not-specific for COVID-19, their detection could guide the ordering clinician to be more vigilant in follow-up testing for the disease. A negative STAT rapid influenza/RSV PCR tests and positive Real-Time Reverse Transcriptase Polymerase Chain Reaction (rRT-PCR) would confirm the diagnosis.