Journal of pediatric orthopedics · 2026
Automated Detection of Pediatric Slipped Capital Femoral Epiphysis: A Deep Learning Approach Using Anatomically Informed Attention Guidance.
СтатьяНазвание и аннотация: Перевод в очереди
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Chakladar Sundeep, Pereira Daniel E, Shariati Javad, Hosseinzadeh Pooya
Аннотация
BACKGROUND: Slipped capital femoral epiphysis (SCFE) is an adolescent hip disorder that is often missed on initial presentation due to subtle radiographic findings, leading to significant complications. Traditional diagnostic methods like Klein's line are subjective and prone to inter-observer variability. This study aimed to develop and validate an attention-guided deep learning model to automatically identify SCFE on pediatric pelvic radiographs.
METHODS: A total of 174 pelvic radiographs (139 training, 35 testing) were retrospectively collected. A two-stage model was developed: first, a U-Net++ model segmented the femoral head to define an anatomic region of focus (ROF). Second, an attention-guided EfficientNet B1 model, trained to focus specifically within the ROF, classified radiographs as "SCFE" or "no SCFE." Model performance was evaluated on the testing set using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
RESULTS: The SCFE detection model demonstrated an AUC of 0.893, accuracy of 91.4%, sensitivity of 93.3%, and specificity of 90.0%. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that the model's predictions were based on the anatomically relevant ROF.
CONCLUSIONS: The attention-guided deep learning model detected SCFE with high diagnostic accuracy. The anatomically informed attention mechanism provides interpretability, improving its utility within clinical settings. This automated tool demonstrates potential as a clinical decision-support system to reduce diagnostic delays and improve the consistency of SCFE detection.
LEVEL OF EVIDENCE: Level III.
METHODS: A total of 174 pelvic radiographs (139 training, 35 testing) were retrospectively collected. A two-stage model was developed: first, a U-Net++ model segmented the femoral head to define an anatomic region of focus (ROF). Second, an attention-guided EfficientNet B1 model, trained to focus specifically within the ROF, classified radiographs as "SCFE" or "no SCFE." Model performance was evaluated on the testing set using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
RESULTS: The SCFE detection model demonstrated an AUC of 0.893, accuracy of 91.4%, sensitivity of 93.3%, and specificity of 90.0%. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that the model's predictions were based on the anatomically relevant ROF.
CONCLUSIONS: The attention-guided deep learning model detected SCFE with high diagnostic accuracy. The anatomically informed attention mechanism provides interpretability, improving its utility within clinical settings. This automated tool demonstrates potential as a clinical decision-support system to reduce diagnostic delays and improve the consistency of SCFE detection.
LEVEL OF EVIDENCE: Level III.