Automatic Facial Paralysis Assessment via Computational Image Analysis.

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Computational Analysis

Background:

• Facial paralysis (FP) significantly impacts patient quality of life.
• Current diagnostic methods, like the House-Brackmann (HB) grading system, are subjective and lack quantitative assessment.
• Objective and automated diagnostic tools are needed for accurate FP evaluation.

Purpose of the Study:

• To develop an efficient and objective approach for assessing facial paralysis severity.
• To utilize computational image analysis for quantitative FP diagnosis.
• To provide an automated alternative to subjective clinical grading systems.

Main Methods:

• Laser speckle contrast imaging was used to measure facial blood flow in FP patients, generating RGB and blood flow images.
• An improved segmentation technique divided the face into regions to extract facial blood flow distribution characteristics.
• Three HB score classifiers were employed to quantify FP severity.

Main Results:

• The proposed method achieved a high accuracy of 97.14% in assessing FP severity on 80 patients.
• Quantitative results demonstrated superior performance compared to state-of-the-art systems.
• The automated approach yielded objective FP diagnosis results consistent with experienced clinician evaluations.

Conclusions:

• The developed computational image analysis approach offers an objective and quantitative method for diagnosing facial paralysis.
• This technique overcomes the subjectivity inherent in traditional FP assessment methods.
• The findings support the potential of this automated system for clinical use in FP diagnosis.