PMID- 34821396
OWN - NLM
STAT- MEDLINE
DCOM- 20220819
LR  - 20221015
IS  - 1531-4995 (Electronic)
IS  - 0023-852X (Print)
IS  - 0023-852X (Linking)
VI  - 132
IP  - 9
DP  - 2022 Sep
TI  - Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: 
      Toward Real-Time Laryngeal Cancer Detection.
PG  - 1798-1806
LID - 10.1002/lary.29960 [doi]
AB  - OBJECTIVES: To assess a new application of artificial intelligence for real-time 
      detection of laryngeal squamous cell carcinoma (LSCC) in both white light (WL) 
      and narrow-band imaging (NBI) videolaryngoscopies based on the You-Only-Look-Once 
      (YOLO) deep learning convolutional neural network (CNN). STUDY DESIGN: 
      Experimental study with retrospective data. METHODS: Recorded videos of LSCC were 
      retrospectively collected from in-office transnasal videoendoscopies and 
      intraoperative rigid endoscopies. LSCC videoframes were extracted for training, 
      validation, and testing of various YOLO models. Different techniques were used to 
      enhance the image analysis: contrast limited adaptive histogram equalization, 
      data augmentation techniques, and test time augmentation (TTA). The 
      best-performing model was used to assess the automatic detection of LSCC in six 
      videolaryngoscopies. RESULTS: Two hundred and nineteen patients were 
      retrospectively enrolled. A total of 624 LSCC videoframes were extracted. The 
      YOLO models were trained after random distribution of images into a training set 
      (82.6%), validation set (8.2%), and testing set (9.2%). Among the various models, 
      the ensemble algorithm (YOLOv5s with YOLOv5m-TTA) achieved the best LSCC 
      detection results, with performance metrics in par with the results reported by 
      other state-of-the-art detection models: 0.66 Precision (positive predicted 
      value), 0.62 Recall (sensitivity), and 0.63 mean Average Precision at 0.5 
      intersection over union. Tests on the six videolaryngoscopies demonstrated an 
      average computation time per videoframe of 0.026 seconds. Three demonstration 
      videos are provided. CONCLUSION: This study identified a suitable CNN model for 
      LSCC detection in WL and NBI videolaryngoscopies. Detection performances are 
      highly promising. The limited complexity and quick computational times for LSCC 
      detection make this model ideal for real-time processing. LEVEL OF EVIDENCE: 3 
      Laryngoscope, 132:1798-1806, 2022.
CI  - (c) 2021 The Authors. The Laryngoscope published by Wiley Periodicals LLC on behalf 
      of The American Laryngological, Rhinological and Otological Society, Inc.
FAU - Azam, Muhammad Adeel
AU  - Azam MA
AD  - Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.
AD  - Department of Informatics, Bioengineering, Robotics, and System Engineering, 
      University of Genoa, Genoa, Italy.
FAU - Sampieri, Claudio
AU  - Sampieri C
AUID- ORCID: 0000-0002-7699-2291
AD  - Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico 
      San Martino, Genoa, Italy.
AD  - Department of Surgical Sciences and Integrated Diagnostics (DISC), University of 
      Genoa, Genoa, Italy.
FAU - Ioppi, Alessandro
AU  - Ioppi A
AUID- ORCID: 0000-0002-2764-715X
AD  - Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico 
      San Martino, Genoa, Italy.
AD  - Department of Surgical Sciences and Integrated Diagnostics (DISC), University of 
      Genoa, Genoa, Italy.
FAU - Africano, Stefano
AU  - Africano S
AD  - Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico 
      San Martino, Genoa, Italy.
AD  - Department of Surgical Sciences and Integrated Diagnostics (DISC), University of 
      Genoa, Genoa, Italy.
FAU - Vallin, Alberto
AU  - Vallin A
AD  - Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico 
      San Martino, Genoa, Italy.
AD  - Department of Surgical Sciences and Integrated Diagnostics (DISC), University of 
      Genoa, Genoa, Italy.
FAU - Mocellin, Davide
AU  - Mocellin D
AD  - Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico 
      San Martino, Genoa, Italy.
AD  - Department of Surgical Sciences and Integrated Diagnostics (DISC), University of 
      Genoa, Genoa, Italy.
FAU - Fragale, Marco
AU  - Fragale M
AD  - Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico 
      San Martino, Genoa, Italy.
AD  - Department of Surgical Sciences and Integrated Diagnostics (DISC), University of 
      Genoa, Genoa, Italy.
FAU - Guastini, Luca
AU  - Guastini L
AD  - Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico 
      San Martino, Genoa, Italy.
AD  - Department of Surgical Sciences and Integrated Diagnostics (DISC), University of 
      Genoa, Genoa, Italy.
FAU - Moccia, Sara
AU  - Moccia S
AD  - The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola 
      Superiore Sant'Anna, Pisa, Italy.
FAU - Piazza, Cesare
AU  - Piazza C
AUID- ORCID: 0000-0002-2391-9357
AD  - Unit of Otorhinolaryngology - Head and Neck Surgery, ASST Spedali Civili of 
      Brescia, Brescia, Italy.
AD  - Department of Medical and Surgical Specialties, Radiological Sciences, and Public 
      Health, University of Brescia, Brescia, Italy.
FAU - Mattos, Leonardo S
AU  - Mattos LS
AUID- ORCID: 0000-0002-8511-9144
AD  - Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.
AD  - Department of Informatics, Bioengineering, Robotics, and System Engineering, 
      University of Genoa, Genoa, Italy.
FAU - Peretti, Giorgio
AU  - Peretti G
AD  - Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico 
      San Martino, Genoa, Italy.
AD  - Department of Surgical Sciences and Integrated Diagnostics (DISC), University of 
      Genoa, Genoa, Italy.
LA  - eng
PT  - Journal Article
DEP - 20211125
PL  - United States
TA  - Laryngoscope
JT  - The Laryngoscope
JID - 8607378
SB  - IM
MH  - Artificial Intelligence
MH  - *Deep Learning
MH  - Humans
MH  - *Laryngeal Neoplasms/diagnostic imaging
MH  - *Laryngoscopes
MH  - Laryngoscopy
MH  - Narrow Band Imaging/methods
MH  - Retrospective Studies
PMC - PMC9544863
OTO - NOTNLM
OT  - Larynx cancer
OT  - computer-assisted image interpretation
OT  - deep learning
OT  - narrow band imaging
OT  - videolaryngoscopy
EDAT- 2021/11/26 06:00
MHDA- 2022/08/20 06:00
PMCR- 2022/10/07
CRDT- 2021/11/25 08:56
PHST- 2021/11/05 00:00 [revised]
PHST- 2021/08/16 00:00 [received]
PHST- 2021/11/15 00:00 [accepted]
PHST- 2021/11/26 06:00 [pubmed]
PHST- 2022/08/20 06:00 [medline]
PHST- 2021/11/25 08:56 [entrez]
PHST- 2022/10/07 00:00 [pmc-release]
AID - LARY29960 [pii]
AID - 10.1002/lary.29960 [doi]
PST - ppublish
SO  - Laryngoscope. 2022 Sep;132(9):1798-1806. doi: 10.1002/lary.29960. Epub 2021 Nov 
      25.