https://www.ncbi.nlm.nih.gov/pubmed/30568383
Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy.
Author information
- 1
- Department of Transplantation, Oslo University Hospital, Oslo 0424, Norway.
- 2
- Center for Digital Engineering Simula Metropolitan, Fornebu 1364, Norway.
Abstract
Assisted diagnosis using artificial intelligence has been a holy grail in medical research for many years, and recent developments in computer hardware have enabled the narrower area of machine learning to equip clinicians with potentially useful tools for computer assisted diagnosis (CAD) systems. However, training and assessing a computer's ability to diagnose like a human are complex tasks, and successful outcomes depend on various factors. We have focused our work on gastrointestinal (GI) endoscopy because it is a cornerstone for diagnosis and treatment of diseases of the GI tract. About 2.8 million luminal GI (esophageal, stomach, colorectal) cancers are detected globally every year, and although substantial technical improvements in endoscopes have been made over the last 10-15 years, a major limitation of endoscopic examinations remains operator variation. This translates into a substantial inter-observer variation in the detection and assessment of mucosal lesions, causing among other things an average polyp miss-rate of 20% in the colon and thus the subsequent development of a number of post-colonoscopy colorectal cancers. CAD systems might eliminate this variation and lead to more accurate diagnoses. In this editorial, we point out some of the current challenges in the development of efficient computer-based digital assistants. We give examples of proposed tools using various techniques, identify current challenges, and give suggestions for the development and assessment of future CAD systems.
KEYWORDS:
Artificial intelligence; Computer assisted diagnosis; Deep learning; Endoscopy; Gastrointestinal
- PMID:
- 30568383
- PMCID:
- PMC6288655
- DOI:
- 10.3748/wjg.v24.i45.5057
WJG 논문
노르웨이 오슬로대
Inter-observer varation 이 있고, 대장내시경시 missing rate 가 20% 정도 되니까
이것을 알고리즘으로 극복하기 위한 노력들..
데이터셋에 대한 정보가 유용함.
Dataset 에 대한 접근성
Dataset 에 대한 Quality
중요한 두가지 팩터에 대한 언급+
그러나,
폴립발견이 중요하긴 한데, 그게 꼭 알고리즘으로 찾아내는 것만이 영향을 줄까?
장정결 안되거나, 대장 스콥을 움직이는 사람의 손이 제대로 못하면, 대장 스콥의 눈을 대신하는 알고리즘이 잘한다고 해도
놓치게 되는 건 마찬가지 아닐까?
내시경할 때, (현재 눈을 대신하겠다는) 현재의 알고리즘, 전부는 아님을 알자. 좀.
201812_WJG_ML algorithm to improve GI endoscopy.pdf
