Towards accurate and efficient diagnoses in nephropathology: An AI-based approach for assessing kidney transplant rejection.

Autor:	Fayzullin A; Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya st., Moscow 119991, Russia.; World-Class Research Center 'Digital Biodesign and Personalized Healthcare, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya st., Moscow 119991, Russia., Ivanova E; Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya st., Moscow 119991, Russia.; B.V.Petrovsky Russian Research Center of Surgery, 2 Abrikosovskiy lane, Moscow 119991, Russia., Grinin V; PJSC VimpelCom, 10 8th March Street, Moscow 127083, Russia., Ermilov D; PJSC VimpelCom, 10 8th March Street, Moscow 127083, Russia., Solovyeva S; B.V.Petrovsky Russian Research Center of Surgery, 2 Abrikosovskiy lane, Moscow 119991, Russia., Balyasin M; Scientific and Educational Resource Center, Peoples' Friendship University of Russia, 6 Miklukho-Maklaya st., Moscow 117198, Russia., Bakulina A; Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya st., Moscow 119991, Russia., Nikitin P; Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya st., Moscow 119991, Russia., Valieva Y; Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya st., Moscow 119991, Russia.; World-Class Research Center 'Digital Biodesign and Personalized Healthcare, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya st., Moscow 119991, Russia., Kalinichenko A; Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya st., Moscow 119991, Russia., Arutyunyan A; PJSC VimpelCom, 10 8th March Street, Moscow 127083, Russia., Lychagin A; Department of Trauma, Orthopedics and Disaster Surgery, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya st., Moscow 119991, Russia., Timashev P; Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya st., Moscow 119991, Russia.; World-Class Research Center 'Digital Biodesign and Personalized Healthcare, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya st., Moscow 119991, Russia.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2024 Aug 16; Vol. 24, pp. 571-582. Date of Electronic Publication: 2024 Aug 16 (Print Publication: 2024).
DOI:	10.1016/j.csbj.2024.08.011
Abstrakt:	The Banff classification is useful for diagnosing renal transplant rejection. However, it has limitations due to subjectivity and varying concordance in physicians' assessments. Artificial intelligence (AI) can help standardize research, increase objectivity and accurately quantify morphological characteristics, improving reproducibility in clinical practice. This study aims to develop an AI-based solutions for diagnosing acute kidney transplant rejection by introducing automated evaluation of prognostic morphological patterns. The proposed approach aims to help accurately distinguish borderline changes from rejection. We trained a deep-learning model utilizing a fine-tuned Mask R-CNN architecture which achieved a mean Average Precision value of 0.74 for the segmentation of renal tissue structures. A strong positive nonlinear correlation was found between the measured infiltration areas and fibrosis, indicating the model's potential for assessing these parameters in kidney biopsies. The ROC analysis showed a high predictive ability for distinguishing between ci and i scores based on infiltration area and fibrosis area measurements. The AI model demonstrated high precision in predicting clinical scores which makes it a promising AI assisting tool for pathologists. The application of AI in nephropathology has a potential for advancements, including automated morphometric evaluation, 3D histological models and faster processing to enhance diagnostic accuracy and efficiency. Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Alexander Arutyunyan reports a relationship with PJSC VimpelCom that includes: employment. Victor Grinin reports a relationship with PJSC VimpelCom that includes: employment. Dmitry Ermilov reports a relationship with PJSC VimpelCom that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2024 The Authors.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu