Deep-Learning Method for the Diagnosis and Classification of Orbital Blowout Fracture Based on Computed Tomography.

BACKGROUND

Blowout fractures (BOFs) are common injuries. Accurate and rapid diagnosis based on computed tomography (CT) is important for proper management. Deep-learning techniques can contribute to accelerating the diagnostic process and supporting timely and accurate management, particularly in environments with limited medical resources.

PURPOSE

The purpose of this retrospective in-silico cohort study was to develop deep-learning models for detecting and classifying BOF using facial CT.

STUDY DESIGN, SETTING, AND SAMPLE: We conducted a retrospective analysis of facial CT from patients diagnosed with BOF involving the medial wall, orbital floor, or both at Konkuk University Hospital between December 2005 and April 2024. Patients with other facial fractures or those involving the superior or lateral orbital walls were excluded.

PREDICTOR VARIABLE

The predictor variables are the outputs as each model's designated categories from the deep-learning models, which include the predicted 1) fracture status (normal or BOF), 2) fracture location (medial, inferior, or inferomedial), and 3) fracture timing (acute or old).

MAIN OUTCOME VARIABLES

The main outcomes were the human assessments serving as the gold standard, including the presence or absence of BOF, fracture location, and timing.

COVARIATES

The covariates were age and sex.

ANALYSES

Model performance was evaluated using the following metrics: 1) accuracy, 2) positive predictive value (PPV), 3) sensitivity, 4) F1 score (harmonic average between PPV and sensitivity), and 5) area under the receiver operating characteristic curve (AUC) for classification models.

RESULTS

This study analyzed 1,264 facial CT from 233 patients with multiple CT slices taken from each patient in various coronal views (mean age: 37.5 ± 17.9 years; 79.8% male-186 subjects). Based on these data, 3 deep-learning models were developed for 1) BOF detection (accuracy 99.5%, PPV 99.2%, sensitivity 99.6%, F1 score 99.4%, AUC 0.9999), 2) BOF location (medial, inferior, or inferomedial; accuracy 97.4%, PPV 92.7%, sensitivity 89.0%, F1 score 90.8%), and 3) BOF timing (accuracy 96.8%, PPV 90.1%, sensitivity 89.7%, F1 score 89.9%). In addition, the BOF detection model had an AUC of 0.9999.

CONCLUSIONS AND RELEVANCE

Deep-learning models developed with Neuro-T (Neurocle Inc, Seoul, Republic of Korea) can reliably diagnose and classify BOF in CT, distinguishing acute from old fractures and aiding clinical decision-making.