JMIR Publications

ABSTRACT

Background:

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 utilized by domain-specific experts (i.e., pathologists, and medical imaging professionals). This study presents an innovative artificial intelligence 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.

Objective:

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.

Methods:

Whole slide images (WSIs) from both public and institutional databases were used to develop a training set for three 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.

Results:

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.

Conclusions:

This study demonstrates how accessible autoML algorithms can readily be used in digital pathology to develop diagnostic AI models using WSIs. Such models could be used by pathologists to improve diagnostic efficiency. Clinical Trial: NA