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VOLUME 21 , ISSUE 8 ( August, 2020 ) > List of Articles


New Classification System for Cleft Alveolus: A Computed Tomography-based Appraisal

Philip Mathew, Vivekanand S Kattimani, Rahul VC Tiwari, Mohammad S Iqbal, Aisha Tabassum, Khalid G Syed

Citation Information : Mathew P, Kattimani VS, Tiwari RV, Iqbal MS, Tabassum A, Syed KG. New Classification System for Cleft Alveolus: A Computed Tomography-based Appraisal. J Contemp Dent Pract 2020; 21 (8):942-948.

DOI: 10.5005/jp-journals-10024-2849

License: CC BY-NC 4.0

Published Online: 12-01-2020

Copyright Statement:  Copyright © 2020; The Author(s).


Aim: The present study proposed a new classification system based on computed tomography (CT) scan appraisal; this enables the surgeon to identify the extent of the defect and helps to execute the proper treatment plan. Background: Various terminologies and classifications were proposed to understand developmental defects. But none of the existing classifications/nomenclatures used the preoperative radiographic evaluation (i.e., computed tomography scan—CT scan) in the management and prognosis. Various treatments were advocated and practiced successfully for the surgical correction of lip and palate. Materials and methods: The available CT scans from archives of the Department of Radiology and Oral and Dental Surgery were evaluated (retrospectively) for cleft alveolus and its morphology as per the proposal. The English language literature was searched in the MEDLINE database without date restriction to revise existing literature on numerous classification systems/nomenclatures using MeSH keywords related to cleft lip, palate, alveolus, developmental disturbance, facial clefts, and classification. Existing classification systems were revisited with a note on the drawbacks. After careful examination of morphological patterns of all clefts, the new CT scan-based alveolar cleft classification is proposed depending on the extent of cleft. Results: The literature revealed a total of twenty-nine classifications of cleft lip and palate starting from the year 1922 to the year 2015, but none exclusively classified the cleft alveolus based on CT scan observations. The observation of three thousand CT scans showed five types of cleft alveolus, depending on the extent of involvement. Conclusion: The CT scan-based classification is essential to the surgeon for successful surgical planning of cleft alveolus. The proposed classification is clinically relevant in this digital era for relating surgical outcomes. The three-dimensional viewing of a defect is essential for the surgeon for virtual planning. This paper provides a CT scan-based classification for universal acceptance in this era of digital technology, and CT scan aids in achieving these goals. Clinical significance: The new proposal is based on preoperative evaluation of cleft using a CT scan. CT scan imaging provides a clear picture of the cleft in three dimensions for the operating surgeon. Advanced technology-enhanced surgical management modalities like CAD/CAM guided templates to support graft for successful management. The classification system will help the medical and surgical fraternity in various aspects. The three-dimensional modeling of defect and printing of a defect model using additive manufacturing technology helps the surgeon for presurgical visualization and virtual planning in a better way. This strategy of defect classification using a CT scan will help obtain better clinical outcomes and patient satisfaction.

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