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uDISE model: a universal drug-induced sedation endoscopy classification system-part 1.

Esuabom DijemeniGabriele D'AmoneIsrael Gbati
Published in: European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery (2017)
Drug-induced sedation endoscopy (DISE) classification systems play a significant role in clinical analysis based on DISE findings, treatment decision process, treatment planning process and fundamentally in treatment outcomes. However, there is a major problem: there is no universally agreed DISE classification system. Hence, for the same DISE examination different DISE classification systems can be used to: assess anatomic findings, decide and plan different treatments. Hence, this leads to different treatment outcomes. The key objective of this study is to propose uDISE model: universal drug-induced sedation endoscopy (DISE) classification system. Set theory and relational mapping was used to develop a DISE classification system based on anatomical structures/level; degree of severity; and configuration of obstruction and its relationship with existing DISE classification systems. uDISE model consists of seven anatomical sites (nose, velum, tonsils, lateral pharyngeal wall/oropharynx, tongue base, epiglottis and larynx), three degrees of obstructive severity (none, partial and complete), three configurations of obstruction (anteroposterior, lateral and circumferential) and a severity index. uDISE model was mapped to four existing DISE classification systems: Pringle and Croft grading system, VOTE, NOHL and P-T-L-Tb-E. uDISE model provides a methodology for mapping different DISE findings based on different classification systems into one common DISE assessments format. This provides a framework for comparing different DISE assessments, treatment plan and treatment outcome irrespective of DISE classification system used. Further research is required to establish a complete relational mapping between uDISE model and other existing DISE classification systems.
Keyphrases
  • drug induced
  • liver injury
  • machine learning
  • deep learning
  • high resolution
  • minimally invasive
  • mycobacterium tuberculosis
  • intensive care unit
  • combination therapy
  • small bowel
  • decision making