"Artificial histology" in colonic Neoplasia: A critical approach.

Autor: Faa G; Department of Medical Sciences and Public Health, Università degli Studi di Cagliari, 09123 Cagliari, Italy; Department of Biology, College of Science and Technology, Temple University, Philadelphia, PA, 19122 USA. Electronic address: gavino.faa@unica.it., Fraschini M; Department of Electrical and Electronic Engineering, Università degli Studi di Cagliari, 09123 Cagliari, Italy. Electronic address: fraschin@unica.it., Didaci L; Department of Electrical and Electronic Engineering, Università degli Studi di Cagliari, 09123 Cagliari, Italy. Electronic address: didaci@unica.it., Saba L; Department of Radiology, University Hospital, Università degli Studi di Cagliari, 40138 Cagliari, Italy. Electronic address: luca.saba@unica.it., Scartozzi M; Medical Oncology Unit, University Hospital of Cagliari, Università degli Studi di Cagliari, 09123 Cagliari, Italy. Electronic address: mario.scartozzi@unica.it., Orvieto E; Department of Pathology, ULSS 8 Berica, San Bortolo Hospital, 36100 Vicenza, Italy. Electronic address: enrico.orvieto@aulss8.veneto.it., Rugge M; Department of Medicine - DIMED; General Anatomic Pathology and Cytopathology Unit, Università degli Studi di Padova, 35121 Padova, Italy. Electronic address: massimo.rugge@unipd.it.
Jazyk: angličtina
Zdroj: Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver [Dig Liver Dis] 2024 Nov 29. Date of Electronic Publication: 2024 Nov 29.
DOI: 10.1016/j.dld.2024.11.001
Abstrakt: Background: The histological assessment of colorectal precancer and cancer lesions is challenging and primarily impacts the clinical strategies of secondary colon cancer prevention. Artificial intelligence (AI) models may potentially assist in the histological diagnosis of this spectrum of phenotypical changes.
Objectives: To provide a current overview of the evidence on AI-based methods for histologically assessing colonic precancer and cancer lesions.
Methods: Based on the available studies, this review focuses on the reliability of AI-driven models in ranking the histological phenotypes included in colonic oncogenesis.
Results: This review acknowledges the efforts to shift from subjective pathologists-based to more objective AI-based histological phenotyping. However, it also points out significant limitations and areas that require improvement.
Conclusions: Current AI-driven methods have not yet achieved the expected level of clinical effectiveness, and there are still significant ethical concerns that need careful consideration. The integration of "artificial histology" into diagnostic practice requires further efforts to combine advancements in engineering techniques with the expertise of pathologists.
Competing Interests: Conflict of interest none of the authors have competing interests to declare
(Copyright © 2024. Published by Elsevier Ltd.)
Databáze: MEDLINE
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