Application of artificial intelligence in assessing the size of colon tumors during endoscopic examination
K. I. Kulaev,
A. V. Vazhenin,
A. V. Privalov,
E. A. Pushkarev,
K. S. Zuykov,
I. A. Popova,
V. E. Samarkin,
E. V. Alkhanov,
D. M. Kutuzov,
A. I. Kulaev,
S. V. Yaytsev https://doi.org/10.21518/ms2026-355
Abstract
Introduction. The risk of malignancy of precancerous colonic lesions directly depends on the size of the neoplasms detected during colonoscopy. According to various authors, neoplasms up to 2.0 cm in diameter have a 12.5% risk of harboring malignant cells. If the neoplasm is larger than 2.0 cm, the risk of malignancy increases significantly and, according to various authors, ranges from 31 to 57%. The endoscopist’s subjective assessment of neoplasm size often does not correspond to the actual size, leading to inappropriate treatment decisions.
Aim. To develop and evaluate the effectiveness of a hardware-based subsystem for measuring the size of colonic neoplasms (AI-assisted neoplasm size estimation, AIANSE) during colonoscopy with AI-assisted colonoscopy detection (AIAC).
Materials and methods. From 2022 to 2024, the Chelyabinsk Regional Clinical Center of Oncology and Nuclear Medicine, in collaboration with the Russian company EVA Lab, developed and implemented a clinical decision support system based on artificial intelligence algorithms. The study included 1,763 patients, from whom approximately 27,000 frames were labeled. Results. Computer processing of endoscopic images demonstrated its effectiveness in both tumor detection and tumor size estimation. This is confirmed by comparing colonoscopy image analysis data with the actual dimensions of the neoplasms determined after surgical treatment. The system possesses self-learning capabilities, as evidenced by the increase in specificity (from 63% to 70%), sensitivity (from 64% to 70%), and accuracy (from 66% to 71%) between 2022 and 2024.
Conclusions. AIANSE, a computer-based tumor analysis and size measurement system, was developed for the first time. This system demonstrated its effectiveness in tumor size estimation, with an accuracy of 71%. This development demonstrated its greatest effectiveness in measuring tumor sizes of 5 mm in diameter, with an accuracy of 76%.
Keywords
neoplasm size measurement by artificial intelligence systems,
artificial intelligence in endoscopy,
new technologies in endoscopy,
colon cancer,
convolutional neural networks,
deep learning,
early colorectal cancer,
colorectal cancer diagnostics,
assisted neoplasm size estimation in colonoscopy,
AI-assisted colonoscopy (AIAC),
AI-assisted neoplasm size estimation (AIANSE)
About the Authors
K. I. Kulaev
Chelyabinsk Regional Clinical Center of Oncology and Nuclear Medicine
Russian Federation
Russian Federation
Konstantin I. Kulaev, Cand. Sci. (Med.), Physician of the Highest Qualifi Category, Head of the Endoscopy Department
42, Blukher St., Chelyabinsk, 454087
A. V. Vazhenin
South Ural State Medical University
Russian Federation
Russian Federation
Andrey V. Vazhenin, Acad. RAS, Dr. Sci. (Med.), Professor, Physician of the Highest Qualification Category, Head of the Department of Oncology, Radiology and Radiation Therapy
64, Vorovskiy St., Chelyabinsk, 454092
A. V. Privalov
South Ural State Medical University
Russian Federation
Russian Federation
Alexey V. Privalov, Dr. Sci. (Med.), Professor, Physician of the Highest Qualification Category, Professor of the Department of Oncology, Radiology and Radiation Therapy
64, Vorovskiy St., Chelyabinsk, 454092
E. A. Pushkarev
Chelyabinsk Regional Clinical Center of Oncology and Nuclear Medicine
Russian Federation
Russian Federation
Evgenii A. Pushkarev, Cand. Sci. (Med.), Endoscopist of the First Qualification Category, Staff Member of the Endoscopy Department
42, Blukher St., Chelyabinsk, 454087
K. S. Zuykov
Chelyabinsk Regional Clinical Center of Oncology and Nuclear Medicine
Russian Federation
Russian Federation
Konstantin S. Zuykov, Endoscopist of the Highest Qualification Category, Staff Member of the Endoscopy Department
42, Blukher St., Chelyabinsk, 454087
I. A. Popova
Chelyabinsk Regional Clinical Center of Oncology and Nuclear Medicine
Russian Federation
Russian Federation
Inna A. Popova, Cand. Sci. (Med.), Endoscopist of the First Qualifi Category, Staff Member of the Endoscopy Department
42, Blukher St., Chelyabinsk, 454087
V. E. Samarkin
Chelyabinsk Regional Clinical Center of Oncology and Nuclear Medicine
Russian Federation
Russian Federation
Vladislav E. Samarkin, Endoscopist, Staff Member of the Endoscopy Department
42, Blukher St., Chelyabinsk, 454087
E. V. Alkhanov
EVA Lab LLC
Russian Federation
Russian Federation
Evgenii V. Alkhanov, General Director
6, Room 158, Ternopolskaya St., Chelyabinsk, 454080
D. M. Kutuzov
EVA Lab LLC
Russian Federation
Russian Federation
Danila M. Kutuzov, Machine Learning Specialist, Employee
6, Room 158, Ternopolskaya St., Chelyabinsk, 454080
A. I. Kulaev
City Clinical Hospital No. 9
Russian Federation
Russian Federation
Aleksandr I. Kulaev, Physician of the Highest Qualification Category, Head of the Endoscopy Department
5, 5th Elektrovoznaya St., Chelyabinsk, 454046
S. V. Yaytsev
South Ural State Medical University
Russian Federation
Russian Federation
Sergey V. Yaytsev, Dr. Sci. (Med.), Professor, Physician of the Highest Qualification Category, Professor of the Department of Oncology, Radiology and Radiation Therapy
64, Vorovskiy St., Chelyabinsk, 454092
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For citations:
Kulaev KI, Vazhenin AV, Privalov AV, Pushkarev EA, Zuykov KS, Popova IA, Samarkin VE, Alkhanov EV, Kutuzov DM, Kulaev AI, Yaytsev SV. Application of artificial intelligence in assessing the size of colon tumors during endoscopic examination. Meditsinskiy sovet = Medical Council. 2026;(10):132-138. (In Russ.) https://doi.org/10.21518/ms2026-355
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