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Videomics: bringing deep learning to diagnostic endoscopy.

Alberto Paderno1, F Christopher Holsinger2, Cesare Piazza1

  • 1Unit 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.

Current Opinion in Otolaryngology & Head and Neck Surgery
|February 17, 2021
PubMed
Summary
This summary is machine-generated.

Videomics, or video-analysis, uses machine learning to enhance diagnostic endoscopy. This emerging field shows promise for improving lesion detection and characterization, potentially impacting cancer patient outcomes.

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Area of Science:

  • Medical technology
  • Artificial intelligence in medicine
  • Endoscopic imaging analysis

Background:

  • Machine learning (ML) algorithms have advanced clinical medicine but have seen limited application in video-endoscopy.
  • Diagnostic endoscopy relies heavily on human interpretation, presenting opportunities for technological augmentation.

Purpose of the Study:

  • To review the emerging field of video-analysis, termed 'Videomics', in diagnostic endoscopy.
  • To assess preliminary findings, potential, and limitations of Videomics.
  • To consider future developments in this area.

Main Methods:

  • Comprehensive review of existing literature on ML applications in video-endoscopy.
  • Analysis of preliminary findings and identified applications of Videomics.
  • Evaluation of current limitations and future research directions.

Main Results:

  • ML has been applied to diagnostic endoscopy for tasks including blind-spot detection, quality control, and lesion detection, classification, and characterization.
  • Applications have expanded from gastrointestinal endoscopy to the upper aerodigestive tract with promising results.
  • Multispectral imaging, such as Narrow Band Imaging, provides significant information from endoscopic images.

Conclusions:

  • Videomics is an emerging discipline with the potential to significantly improve lesion detection and characterization during endoscopy.
  • Standardization of data collection, common targets, and reporting are crucial for advancing Videomics.
  • Collaborative efforts in Videomics are expected to augment clinical endoscopy and improve cancer patient outcomes.