Characterizing hip joint morphology using a multitask deep learning model | JoVE Visualize

Area of Science:

Artificial Intelligence in Medicine
Medical Imaging Analysis
Orthopedic Diagnostics

Background:

Deep learning (DL) significantly advances medical imaging analysis for classifying pathoanatomical conditions.
Limited accurate and efficient machine learning (ML) algorithms exist for diagnosing morphological hip pathologies, such as developmental dysplasia of the hip (DDH) and femoroacetabular impingement (FAI).
Accurate diagnosis of these conditions is crucial for effective patient management and treatment planning.

Purpose of the Study:

To evaluate the performance of a novel ML model utilizing YOLOv5 and ConvNeXt-Tiny architectures.
To predict key morphological features associated with DDH and FAI, including cam deformity, ischial spine sign, and dysplastic appearance.
To assess the model's diagnostic accuracy and compare it with inter-rater reliability among orthopedic surgeons.

Main Methods:

Development and implementation of a novel ML model integrating YOLOv5 and ConvNeXt-Tiny architectures.
Training and validation of the model on a dataset of hip imaging to identify specific morphological abnormalities.
Evaluation of model performance using accuracy metrics and Area Under the Receiver Operating Curve (AUC). Inter-rater agreement was assessed using Gwet's AC1 statistic.

Main Results:

The ML model achieved high accuracy in detecting specific hip pathologies: 78.0% for cam deformity, 87.2% for ischial spine sign, and 76.6% for dysplasia.
The model demonstrated strong discriminative ability with AUC values of 0.80 for cam deformity, 0.89 for ischial spine sign, 0.80 for dysplasia, and 0.81 for all abnormalities combined.
Inter-rater agreement among surgeons was substantial for dysplasia (0.83) and all abnormalities (0.88), and moderate for ischial spine sign (0.75) and cam deformity (0.61).

Conclusions:

The novel ML model shows significant potential for accurately identifying morphological hip abnormalities.
This deep learning approach offers a promising tool for enhancing the diagnostic workup of conditions like DDH and FAI.
The model's performance suggests it could aid clinicians in the efficient and reliable diagnosis of hip pathologies.