Currently submitted to: JMIR AI
Date Submitted: Aug 28, 2025
Open Peer Review Period: Jul 14, 2026 - Sep 8, 2026
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Panognostics: A Novel Intelligent Tablet Application for Real-Time Automated Dental Charting and Structured Reporting on Panoramic Radiographs
ABSTRACT
Background:
Panoramic radiographs are essential in dental diagnostics. They provide a broad overview of the oral and maxillofacial region. However, their interpretation is often limited by overlapping structures, patient positioning issues, and low spatial resolution. Moreover, reliance on manual analysis can be highly time-consuming, and subject to diagnostic inconsistency. These challenges highlight the need for an intelligent system that can automate interpretation and support consistency and accuracy in clinical decision-making.
Objective:
This study presents Panognostics, a novel cloud-based, tablet-compatible AI application developed to automate the interpretation of panoramic radiographs through detection, segmentation, charting, and structured reporting of key anatomical structures. The system is designed to enhance diagnostic accuracy, streamline reporting, and diagnostic support through an intuitive, real-time interface optimized for use in chairside clinical settings.
Methods:
An ethically approved dataset of 2,768 panoramic radiographs was used to develop and validate a two-stage YOLOv11-based pipeline for detecting and segmenting teeth, periodontal bone level, and the maxillary sinus floor. Performance was evaluated using precision, sensitivity, and mAP. A clinical inference module was developed to classify maxillary posterior teeth as either overlapping or in proximity to the sinus floor, tested on 40 panoramic radiographs (399 maxillary posterior teeth). Interpretations were visualized on an interactive dental chart and summarized in an automatically generated structured report. Application response time was recorded to assess real-time feasibility.
Results:
The application demonstrated high detection (mAP@0.5: 0.96–0.97) and segmentation performance, with precision and sensitivity above 0.94 for all dental and bony structures. Sinus segmentation reached 0.98 precision and 0.99 sensitivity, while tooth and periodontal bone level segmentation achieved mAP@0.5 of 0.92. Among 399 MT analysed, molars accounted for over 65% of sinus involvement, while premolars and canines were more often associated with sinus proximity. Panognostics’ average response time was under 16 seconds.
Conclusions:
Panognostics offers accurate, fast, and interpretable AI-based detection and segmentation of teeth, periodontal bone level, and the maxillary sinus floor, enabling automated creation of an interactive dental chart and a standardized structured report. This tool allows efficient visualization and documentation of panoramic radiograph findings through a scalable, tablet-compatible platform. Optimized for both professional decision-support and patient communication, Panognostics integrates easily into daily dental workflow, underscoring the emphasizing the innovative potential of tablet-based AI solutions in modern oral healthcare.
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