# Ocular-Induced Abnormal Head Posture: Diagnosis and Missing Data Imputation

arXiv: https://arxiv.org/abs/2510.05649 · HTML: https://mlsanity.com/paper/2510.05649/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 56 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Gated Multimodal Units for Information Fusion](https://mlsanity.com/paper/1702.01992/) (2017)
- [Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs](https://mlsanity.com/paper/1907.06724/) (2019)
- [Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review](https://mlsanity.com/paper/1908.05780/) (2019)
- [Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking](https://mlsanity.com/paper/2302.10902/) (2023)
- [Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?](https://mlsanity.com/paper/2304.11749/) (2023)
- [Learnable Prompt as Pseudo-Imputation: Rethinking the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction](https://mlsanity.com/paper/2401.16796/) (2024)
- [On the Performance of Imputation Techniques for Missing Values on Healthcare Datasets](https://mlsanity.com/paper/2403.14687/) (2024)
- [HeadPosr: End-to-end Trainable Head Pose Estimation using Transformer Encoders](https://mlsanity.com/paper/2202.03548/) (2022)
- [PoseGaze-AHP: A Knowledge-Based 3D Dataset for AI-Driven Ocular and Postural Diagnosis](https://mlsanity.com/paper/2510.03873/) (2025)

## Abstract (arXiv)

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.