Alberto Paderno1, F Christopher Holsinger, Cesare Piazza. 1. Unit of Otorhinolaryngology - Head and Neck Surgery, ASST Spedali Civili of Brescia, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy Division of Head and Neck Surgery, Stanford University, Palo Alto, California, USA.
Abstract
PURPOSE OF REVIEW: Machine learning (ML) algorithms have augmented human judgment in various fields of clinical medicine. However, little progress has been made in applying these tools to video-endoscopy. We reviewed the field of video-analysis (herein termed 'Videomics' for the first time) as applied to diagnostic endoscopy, assessing its preliminary findings, potential, as well as limitations, and consider future developments. RECENT FINDINGS: ML has been applied to diagnostic endoscopy with different aims: blind-spot detection, automatic quality control, lesion detection, classification, and characterization. The early experience in gastrointestinal endoscopy has recently been expanded to the upper aerodigestive tract, demonstrating promising results in both clinical fields. From top to bottom, multispectral imaging (such as Narrow Band Imaging) appeared to provide significant information drawn from endoscopic images. SUMMARY: Videomics is an emerging discipline that has the potential to significantly improve human detection and characterization of clinically significant lesions during endoscopy across medical and surgical disciplines. Research teams should focus on the standardization of data collection, identification of common targets, and optimal reporting. With such a collaborative stepwise approach, Videomics is likely to soon augment clinical endoscopy, significantly impacting cancer patient outcomes.
PURPOSE OF REVIEW: Machine learning (ML) algorithms have augmented human judgment in various fields of clinical medicine. However, little progress has been made in applying these tools to video-endoscopy. We reviewed the field of video-analysis (herein termed 'Videomics' for the first time) as applied to diagnostic endoscopy, assessing its preliminary findings, potential, as well as limitations, and consider future developments. RECENT FINDINGS: ML has been applied to diagnostic endoscopy with different aims: blind-spot detection, automatic quality control, lesion detection, classification, and characterization. The early experience in gastrointestinal endoscopy has recently been expanded to the upper aerodigestive tract, demonstrating promising results in both clinical fields. From top to bottom, multispectral imaging (such as Narrow Band Imaging) appeared to provide significant information drawn from endoscopic images. SUMMARY: Videomics is an emerging discipline that has the potential to significantly improve human detection and characterization of clinically significant lesions during endoscopy across medical and surgical disciplines. Research teams should focus on the standardization of data collection, identification of common targets, and optimal reporting. With such a collaborative stepwise approach, Videomics is likely to soon augment clinical endoscopy, significantly impacting cancerpatient outcomes.
Authors: Muhammad Adeel Azam; Claudio Sampieri; Alessandro Ioppi; Pietro Benzi; Giorgio Gregory Giordano; Marta De Vecchi; Valentina Campagnari; Shunlei Li; Luca Guastini; Alberto Paderno; Sara Moccia; Cesare Piazza; Leonardo S Mattos; Giorgio Peretti Journal: Front Oncol Date: 2022-06-01 Impact factor: 5.738
Authors: Alberto Paderno; Cesare Piazza; Francesca Del Bon; Davide Lancini; Stefano Tanagli; Alberto Deganello; Giorgio Peretti; Elena De Momi; Ilaria Patrini; Michela Ruperti; Leonardo S Mattos; Sara Moccia Journal: Front Oncol Date: 2021-03-24 Impact factor: 6.244