1. Goyal, H., Mann, R., Gandhi, Z., Perisetti, A., Zhang, Z., Sharma, N., Saligram, S., Inamdar, S., and Tharian, B., “Application of artificial intelligence in pancreaticobiliary diseases,” Ther. Adv. Gastrointest. Endosc., 14, 1–12 (2021).
2. Hameed BS, Krishnan UM (2022) Artificial intelligence-driven diagnosis of pancreatic cancer. Cancers (basel) 14(21):5382
3. Protasova, Z. U., Shatalova, O. V., Dafalla, A. A. B., and Degtyarev, S. V., “Methods and algorithms for the formation of weak classifiers in an ensemble of classifiers for predicting cardiovascular risks,” Izv. Yug. Zapadn. Gos. Univ. Ser. Upravl. Vychisl. Tekhn. Inform. Med.. Priborostr., 9, No. 3 (32), 64–83 (2019).
4. Chen PT, Chang D, Wu T, Wu MS, Wang W, Liao WC (2021) Applications of artificial intelligence in pancreatic and biliary diseases. J Gastroenterol Hepatol 36(2):286–294
5. Filist SA, Kondrashov DS, Sukhomlinov A (2023) Yu., Shul’ga, L. V., Al’-Darradzhi, Ch. Kh., and Belozerov, V. A., “An automated system for classifying ultrasound images of the pancreas based on segment-by-segment spectral analysis,”. Model Optim Informats Tekhnol 11(4):1–19