Ocular-Induced Abnormal Head Posture: Diagnosis and Missing Data Imputation
This work addresses the need for objective and robust diagnostic tools in ophthalmology, particularly for patients with strabismus, by providing automated solutions that improve upon subjective clinical assessments and incomplete records.
This study tackled the problem of diagnosing ocular-induced abnormal head posture (AHP) and handling missing data in medical records by developing two deep learning frameworks: AHP-CADNet for automated diagnosis achieved 96.9-99.0% accuracy, and a curriculum learning-based imputation framework maintained 93.46-99.78% accuracy in recovering missing data.
Ocular-induced abnormal head posture (AHP) is a compensatory mechanism that arises from ocular misalignment conditions, such as strabismus, enabling patients to reduce diplopia and preserve binocular vision. Early diagnosis minimizes morbidity and secondary complications such as facial asymmetry; however, current clinical assessments remain largely subjective and are further complicated by incomplete medical records. This study addresses both challenges through two complementary deep learning frameworks. First, AHP-CADNet is a multi-level attention fusion framework for automated diagnosis that integrates ocular landmarks, head pose features, and structured clinical attributes to generate interpretable predictions. Second, a curriculum learning-based imputation framework is designed to mitigate missing data by progressively leveraging structured variables and unstructured clinical notes to enhance diagnostic robustness under realistic data conditions. Evaluation on the PoseGaze-AHP dataset demonstrates robust diagnostic performance. AHP-CADNet achieves 96.9-99.0 percent accuracy across classification tasks and low prediction errors for continuous variables, with MAE ranging from 0.103 to 0.199 and R2 exceeding 0.93. The imputation framework maintains high accuracy across all clinical variables (93.46-99.78 percent with PubMedBERT), with clinical dependency modeling yielding significant improvements (p < 0.001). These findings confirm the effectiveness of both frameworks for automated diagnosis and recovery from missing data in clinical settings.