Automating Colon Polyp Classification in Digital Pathology by Evaluation of a “Machine Learning as a Service” AI Model: Algorithm Development and Validation Study

Artificial intelligence (AI) models are increasingly being developed to improve the efficiency of pathological diagnoses. Rapid technological advancements are leading to more widespread availability of AI models that can be used by domain-specific experts (ie, pathologists and medical imaging profes...

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Vydáno v:JMIR formative research Ročník 9; s. e67457
Hlavní autoři: Beyer, David, Delancey, Evan, McLeod, Logan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Canada JMIR Publications 31.07.2025
Témata:
Algorithms
Artificial Intelligence
Colonic Polyps - classification
Colonic Polyps - diagnosis
Colonic Polyps - pathology
Datasets
Humans
Machine Learning
Medical screening
Original Paper
Pathology
Polyps
Sensitivity and Specificity
effectiveness
detection
development
screening
cancer screening
pathology
colon cancer
colon cancer screening
AI
AI models
develop
machine learning
imaging
artificial intelligence
colon polyp
digital pathology
colon
whole slide imaging
applicability
cancer
model
pathologist
automated detection
ISSN:2561-326X, 2561-326X
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Popis
Shrnutí:Artificial intelligence (AI) models are increasingly being developed to improve the efficiency of pathological diagnoses. Rapid technological advancements are leading to more widespread availability of AI models that can be used by domain-specific experts (ie, pathologists and medical imaging professionals). This study presents an innovative AI model for the classification of colon polyps, developed using AutoML algorithms that are readily available from cloud-based machine learning platforms. Our aim was to explore if such AutoML algorithms could generate robust machine learning models that are directly applicable to the field of digital pathology. The objective of this study was to evaluate the effectiveness of AutoML algorithms in generating robust machine learning models for the classification of colon polyps and to assess their potential applicability in digital pathology. Whole-slide images from both public and institutional databases were used to develop a training set for 3 classifications of common entities found in colon polyps: hyperplastic polyps, tubular adenomas, and normal colon. The AI model was developed using an AutoML algorithm from Google's VertexAI platform. A test subset of the data was withheld to assess model accuracy, sensitivity, and specificity. The AI model displayed a high accuracy rate, identifying tubular adenoma and hyperplastic polyps with 100% success and normal colon with 97% success. Sensitivity and specificity error rates were very low. This study demonstrates how accessible AutoML algorithms can readily be used in digital pathology to develop diagnostic AI models using whole-slide images. Such models could be used by pathologists to improve diagnostic efficiency.
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ISSN:2561-326X
2561-326X
DOI:10.2196/67457