Videomics : bringing deep learning to diagnostic endoscopy
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved..
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.
Medienart: |
E-Artikel |
---|
Erscheinungsjahr: |
2021 |
---|---|
Erschienen: |
2021 |
Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
---|---|
Enthalten in: |
Current opinion in otolaryngology & head and neck surgery - 29(2021), 2 vom: 01. Apr., Seite 143-148 |
Sprache: |
Englisch |
---|
Beteiligte Personen: |
Paderno, Alberto [VerfasserIn] |
---|
Links: |
---|
Themen: |
---|
Anmerkungen: |
Date Completed 25.10.2021 Date Revised 25.10.2021 published: Print Citation Status MEDLINE |
---|
doi: |
10.1097/MOO.0000000000000697 |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
NLM32154918X |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | NLM32154918X | ||
003 | DE-627 | ||
005 | 20231225180037.0 | ||
007 | cr uuu---uuuuu | ||
008 | 231225s2021 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1097/MOO.0000000000000697 |2 doi | |
028 | 5 | 2 | |a pubmed24n1071.xml |
035 | |a (DE-627)NLM32154918X | ||
035 | |a (NLM)33595977 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Paderno, Alberto |e verfasserin |4 aut | |
245 | 1 | 0 | |a Videomics |b bringing deep learning to diagnostic endoscopy |
264 | 1 | |c 2021 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ƒaComputermedien |b c |2 rdamedia | ||
338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
500 | |a Date Completed 25.10.2021 | ||
500 | |a Date Revised 25.10.2021 | ||
500 | |a published: Print | ||
500 | |a Citation Status MEDLINE | ||
520 | |a Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved. | ||
520 | |a 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 | ||
520 | |a 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 | ||
520 | |a 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 | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a Review | |
700 | 1 | |a Holsinger, F Christopher |e verfasserin |4 aut | |
700 | 1 | |a Piazza, Cesare |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Current opinion in otolaryngology & head and neck surgery |d 1998 |g 29(2021), 2 vom: 01. Apr., Seite 143-148 |w (DE-627)NLM093606923 |x 1531-6998 |7 nnns |
773 | 1 | 8 | |g volume:29 |g year:2021 |g number:2 |g day:01 |g month:04 |g pages:143-148 |
856 | 4 | 0 | |u http://dx.doi.org/10.1097/MOO.0000000000000697 |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a GBV_NLM | ||
951 | |a AR | ||
952 | |d 29 |j 2021 |e 2 |b 01 |c 04 |h 143-148 |