Artificial intelligence for the prevention and prediction of colorectal neoplasms

Colonoscopy is a useful as a cancer screening test. However, in countries with limited medical resources, there are restrictions on the widespread use of endoscopy. Non-invasive screening methods to determine whether a patient requires a colonoscopy are thus desired. Here, we investigated whether ar...

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Bibliographic Details
Published in:Journal of translational medicine 2023-07, Vol.21 (1), p.431-431, Article 431
Main Authors: Tokutake, Kohjiro, Morelos-Gomez, Aaron, Hoshi, Ken-Ichi, Katouda, Michio, Tejima, Syogo, Endo, Morinobu
Format: Article
Language:English
Subjects:
Adenoma
Analysis
Artificial Intelligence
Blood data
Colonic Polyps
Colonoscopy
Colorectal cancer
Colorectal Neoplasms - diagnosis
Colorectal Neoplasms - prevention & control
CT imaging
Female
Health aspects
Humans
Machine learning
Male
Mass Screening - methods
Methods
Prevention
Risk factors
Screening
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Summary:Colonoscopy is a useful as a cancer screening test. However, in countries with limited medical resources, there are restrictions on the widespread use of endoscopy. Non-invasive screening methods to determine whether a patient requires a colonoscopy are thus desired. Here, we investigated whether artificial intelligence (AI) can predict colorectal neoplasia. We used data from physical exams and blood analyses to determine the incidence of colorectal polyp. However, these features exhibit highly overlapping classes. The use of a kernel density estimator (KDE)-based transformation improved the separability of both classes. Along with an adequate polyp size threshold, the optimal machine learning (ML) models' performance provided 0.37 and 0.39 Matthews correlation coefficient (MCC) for the datasets of men and women, respectively. The models exhibit a higher discrimination than fecal occult blood test with 0.047 and 0.074 MCC for men and women, respectively. The ML model can be chosen according to the desired polyp size discrimination threshold, may suggest further colorectal screening, and possible adenoma size. The KDE feature transformation could serve to score each biomarker and background factors (health lifestyles) to suggest measures to be taken against colorectal adenoma growth. All the information that the AI model provides can lower the workload for healthcare providers and be implemented in health care systems with scarce resources. Furthermore, risk stratification may help us to optimize the efficiency of resources for screening colonoscopy.
ISSN:1479-5876
1479-5876
DOI:10.1186/s12967-023-04258-5