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Bridging the Disciplinary gap for Computer Aided Diagnosis in Colonoscopy: Detection, Evaluation and Diagnostics


The main objective of this Ph.D. project is to create and evaluate a Computer-aided Diagnosis (CADx) system using Artificial Intelligence (AI) to recognise and classify polyps and other pathological findings in real-time during colonoscopies, and researching understandable methods for evaluating the results. As a method for creating the AI models, we will use deep neural networks (DNNs) which are state of the art in image and video related analysis. The goal is to largely eliminate the rate of missed lesions in such examinations and thus increase the number of detected lesions, possibly preventing it from evolving into cancer. Our system is covering the whole pipeline, from extracting videos from the colonoscopy rack, through real-time detection and classification of lesions, to immediate feedback to the clinician. The intermediate feedback during the assessment of the patient is an important part of the project since it will allow the clinician to react immediately on the detected findings. To make sure the results of the analysis is relatable for all the involved disciplines, we will research and develop different ways to evaluate the results, this includes quantifying the effect in clinical trials. We will also look into automatic report generation, with suggested text and images for the medical records, saving time for the doctor.

Project leader: Andreas Petlund

Started: 2019

Ends: 2022

Category: Næringsliv

Sector: Næringsliv

Budget: 2292000


Address: Oslo