Building on the opportunity to improve early detection of lung cancer, this community-driven project aims to develop an end-to-end application that connects the predictive power of machine learning with functional software tested against errors and a clean user interface focused on clinical use.
The application will focus on three big challenges that can help radiologists detecting lung cancer in practice:
Analyze CT scan images to detect and pinpoint the location of concerning nodules from background tissue.
Use what we’ve seen from nodules in the past to predict whether the identified nodules are cancerous or benign.
Find the boundaries of nodules and create automatic measures to help radiologists refine and build out the computer-aided diagnosis.
Bob is 55 years old. He goes to his family doctor for a routine checkup. The family doctor notes that Bob fits the right diagnostic profile for getting a lung cancer early screening scan. Bob’s doctor schedules him for a low dose computed tomography (LDCT) scan at a teaching hospital down the street. Bob goes to this appointment a few days later where a technician helps him into the computed tomography (CT) machine and then captures imagery of his chest cavity.
Dr. Smith is a chief resident in diagnostic radiology at the hospital where Bob’s scans were taken, and works in a lab that conducts experimental research using new technologies and treatments. She is an experienced and competent professional with years of experience making diagnoses using standard Picture Archiving and Communication System (PACS) software.
Still, like most radiologists she acknowledges that there is an art to detection and diagnosis and that if several other radiologists examined the same CT scans they might all reach slightly different conclusions.
She also knows that computer aided detection (CADe)/computer aided diagnosis (CADx) technology has been advancing rapidly and she’s open to using an easy-to-use tool that will help her do her job more efficiently.
Dr. Smith has just pulled up Bob’s imagery in order to review and interpret the imagery that the CT scanner generated. In front of Dr. Smith are two computers:
Here are some issues in the current PACS diagnosis workflow that Dr. Smith feels could be improved upon:
Read more about the scope, architecture, and technical requirements of the application in the project's design document.Next step: making contributions →