Use of Deep Learning Models in the Diagnosis of Proptosis Through Orbital Magnetic Resonance Imaging

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: DenseNet121 achieved 95.0% accuracy

• Primary Outcome: Diagnostic accuracy for proptosis detection using deep learning models

• Nnt Or Sensitivity: Sensitivity 92.7%, Specificity 96.9%, AUC 0.986

• Confidence Interval: Not reported

Clinical Application

Requires specialized MRI protocols and computational infrastructure; implementation challenges in routine practice Would need TGA approval as diagnostic software; integration with existing radiology workflows; cost-effectiveness assessment required for PBS consideration Patients requiring orbital imaging assessment for suspected proptosis

Abstract

BACKGROUND Proptosis is a common manifestation of orbital disease; however, current diagnostic tools, such as the Hertel exophthalmometer and manual radiological measurements, have limited reproducibility and are observer-dependent. More objective, automated approaches are needed. In this single-center retrospective study, orbital magnetic resonance imaging (MRI) examinations from 521 participants (261 with proptosis, 260 controls) were analyzed. Proptosis was defined on MRI using interzygomatic line-based distance criteria. Three-dimensional convolutional neural network models based on DenseNet121, DenseNet169, DenseNet264, and ResNet50 architectures were trained on volumetric orbital MRI data. MATERIAL AND METHODS Data were divided into training, validation, and test sets, and 5-fold cross-validation with early stopping was used to optimize and validate model performance. Diagnostic performance was assessed using accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS DenseNet121 achieved the best overall performance, with mean accuracy of 95.0%, AUC of 0.986, sensitivity of 92.7%, and specificity of 96.9% across 5-fold cross-validation. CONCLUSIONS To the best of our knowledge, prior artificial intelligence studies in orbital imaging have primarily focused on CT-based measurements, radiomics approaches, or thyroid-associated orbitopathy assessment rather than end-to-end 3-dimensional deep learning analysis of orbital MRI volumes. In this context, the present study explores a volumetric MRI-based deep learning framework for automated proptosis detection, emphasizing patient-level classification and explainable model interpretation.