Tag: CBCT

  • REVOLUTIONISING DENTAL CARE THROUGH ARTIFICIAL INTELLIGENCE: APPLICATIONS IN ORAL SURGERY AND DIAGNOSTICS

    Special Edition: Artificial Intelligence and Machine Learning for Modern Systems

    Original Research Article

    Authors: Er. Rishabh Aryan1*, Prof. Dr. Tryambak Hiwarkar2

    1M.Tech (Artificial Intelligence and Data Science), Department of Computer Science and Engineering,

    Indian Institute of Information Technology, Bhagalpur (Bihar), India

    2Director, ASM Group of Institutions, Pune, Maharashtra, India

    E-mail: rishabh.250201011@iiitbh.ac.in  |  tryambakhiwarkar@asmedu.org

    *Corresponding author: rishabh.250201011@iiitbh.ac.in

    Abstract

    Artificial Intelligence (AI) is catalysing a paradigm shift in dental medicine, particularly in the domains of oral surgery and clinical diagnostics. This original research study presents a prospective, multi-centre evaluation of AI-assisted diagnostic and surgical-planning systems applied to 1,840 dental patients across four tertiary care centres between January 2022 and December 2024. Employing convolutional neural networks (CNN), residual deep learning (ResNet-50), long short-term memory (LSTM) architectures, and support vector machines (SVM), the integrated AI platform demonstrated diagnostic accuracy of 94.2% for carious lesion detection, 91.8% for periapical pathology, 93.5% for early-stage oral mucosal cancer screening, and 89.4% for implant site assessment — all significantly exceeding mean clinician baselines (p < 0.001). AI-assisted surgical planning reduced pre-operative planning time by 73.6% for implant placement and 71.1% for orthognathic surgery procedures. Patient-reported outcomes (PROs) improved significantly, with post-operative pain VAS scores reduced by 28.4% and complication rates declining from 7.2% to 3.1% in the AI-guided cohort. Radiomics-driven analysis of 11,500 CBCT volumes and 34,000 periapical radiographs formed the annotated dataset backbone. This study provides high-level evidence for the clinical utility, safety, and efficiency of AI integration in contemporary dental practice, while also identifying ethical and regulatory challenges for future deployment.

    Keywords: Artificial Intelligence, Dental Diagnostics, Oral Surgery, Convolutional Neural Networks, CBCT, Deep Learning, Radiomics, Implant Planning, Oral Cancer Screening, Periodontal Assessment.

    Received: 17/05/2026

    Accepted: 04/06/2026

    Published: 07/06/2026

    DOI: 10.37067/ralap.2026.aimlfms.spec.01

    Citation: Aryan, R., & Hiwarkar, T. (2026). Revolutionising dental care through artificial intelligence: Applications in oral surgery and diagnostics. Revista Latinoamericana de la Papa, 1 [Special Edition: Artificial Intelligence and Machine Learning for Modern Systems], 1–10. Available online at https://ojs.papaslatina.org/revolutionising-dental-care-through-artificial-intelligence-applications-in-oral-surgery-and-diagnostics/

    Author(s) Retains the Copyrights of This Article