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Updated: Jun 11, 2026
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Automatic detection of panoramic positioning errors and development of an objective image quality scoring system
Sijia Hu1, Erkang Tian1, Xinze Wu1
1State Key Laboratory of Oral Diseases, West China School of Stomatology, National Clinical Research Center for Oral Diseases, Med-X Center for Manufacturing, Sichuan University, Chengdu, 610064, China.
Objectives:
To develop a deep learning model capable of automatically detecting common positioning errors in panoramic radiographs and to establish an error-based objective ten-point image quality scoring system aligned with expert evaluation.
Methods:
A total of 4,390 panoramic radiographs were retrospectively annotated for seven clinically relevant positioning errors (including chin position, head rotation/tilt, bite position, tongue position, cervical spine positioning, and anatomical coverage) associated with diagnostically meaningful geometric distortion or anatomical superimposition by two experienced oral radiologists. A multi-label deep learning model based on a modified residual network was trained to detect all errors simultaneously. An independent dataset of 510 radiographs was scored for subjective image quality on a ten-point scale. Multiple linear regression was used to estimate the independent contribution of each positioning error to subjective image quality and to derive an objective scoring system established by converting regression-derived weights of significant errors into standardized point deductions.
Results:
High annotation and scoring reliability were achieved (Kappa: 0.879-0.953; ICC: 0.838). The model achieved high performance across all error types, with accuracy ranging from 88.40% to 96.00% and area under the curve values between 98.17 and 99.37. Several positioning errors were associated with lower subjective image quality scores (p < 0.001). The resulting error-weighted objective score showed moderate correlation with expert ratings (r = 0.673, p < 0.001).
Conclusions:
The proposed system enables accurate detection of positioning errors and provides a structured and interpretable, error-based surrogate measure of perceived panoramic image quality.
Advances In Knowledge:
This study presents an error-specific, data-driven framework for objective assessment of panoramic image acquisition quality, enhancing the interpretability and standardization of image quality evaluation.
