Automatic Assessment Of Hip Effusion From MRI

Area of Science:

• Medical imaging analysis
• Artificial intelligence in healthcare
• Osteoarthritis research

Background:

• Joint effusion is a key indicator of osteoarthritis (OA) severity, linked to pain and disability.
• Accurate quantification of joint effusion in Magnetic Resonance Imaging (MRI) is challenging and prone to variability.
• Current methods for effusion assessment lack precision, impacting OA diagnosis and treatment monitoring.

Purpose of the Study:

• To develop and validate a novel deep learning (DL) approach for automatic effusion assessment in hip MRIs.
• To utilize volumetric quantification measures (VQM) for precise and reproducible effusion measurement.
• To reduce inter-observer variability and expert time in OA biomarker assessment.

Main Methods:

• A multiplane ensemble convolutional neural network (CNN) was developed for bony anatomy localization and effusion region detection.
• CNNs were trained on manually segmented femoral head and effusion regions from 3856 hip MRI images (63 patients).
• Validation was performed on 2040 images (34 patients), assessing DL performance using Dice score, sensitivity, precision, and Intraclass correlation coefficient (ICC).

Main Results:

• The DL approach demonstrated high agreement with ground-truth, achieving a Dice score of 0.85, sensitivity of 0.86, and precision of 0.83.
• Per-patient volumetric quantification measures (VQM) showed high agreement between DL and expert assessments (ICC = 0.88).
• The developed technique effectively automates effusion assessment, reducing variability compared to traditional methods.

Conclusions:

• The proposed DL technique offers an accurate and reliable method for volumetric effusion assessment from hip MRIs.
• This automated approach is expected to minimize inter-observer variability in OA biomarker assessment.
• The technique has the potential to improve the quality of osteoarthritis care by providing more reliable indicators of disease progression.