Quantitative assessment of self-treated canalith repositioning procedures using inertial measurement unit sensors.

BACKGROUND: Low success and high recurrence of benign paroxysmal positional vertigo (BPPV) after home-based self-treated Epley and Barbeque (BBQ) roll maneuvers is an important issue. OBJECTIVE: To quantify the cause of low success rate of self-treated Epley and BBQ roll maneuvers and provide a clinically acceptable criterion to guide self-treatment head rotations. METHODS: Twenty-five participants without active BPPV wore a custom head-mount rotation monitoring device for objective measurements. Self-treatment and specialist-assisted maneuvers were compared for head rotation accuracy. Absolute differences between the head rotation evaluation criteria (American Academy of Otolaryngology guidelines) and measured rotation angles were considered as errors. Self-treatment and specialist-treated errors in maneuvers were compared. Between-trial variations and age effects were evaluated. RESULTS: A significantly large error and between-trial variation occurred in step 4 of the self-treated Epley maneuver, with a considerable error in the second trial. The cumulative error of all steps of self-treated BBQ roll maneuver was significantly large. Age effect occurred only in the self-treated BBQ roll maneuver. Errors in specialist-treated maneuvers ranged from 10 to 20 degrees. CONCLUSIONS: Real-time feedback of head movements during simultaneous head-body rotations could increase success rates of self-treatments. Specialist-treated maneuvers can be used as permissible rotation margin criteria.

Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach.

Positional cranial deformities are relatively common conditions, characterized by asymmetry and changes in skull shape. Although three-dimensional (3D) scanning is the gold standard for diagnosing such deformities, it requires expensive laser scanners and skilled maneuvering. We therefore developed an inexpensive, fast, and convenient screening method to classify cranial deformities in infants, based on single two-dimensional vertex cranial images. In total, 174 measurements from 80 subjects were recorded. Our screening software performs image processing and machine learning-based estimation related to the deformity indices of the cranial ratio (CR) and cranial vault asymmetry index (CVAI) to determine the severity levels of brachycephaly and plagiocephaly. For performance evaluations, the estimated CR and CVAI values were compared to the reference data obtained using a 3D cranial scanner. The CR and CVAI correlation coefficients obtained via support vector regression were 0.85 and 0.89, respectively. When the trained model was evaluated using the unseen test data for the three CR and three CVAI classes, an 86.7% classification accuracy of the proposed method was obtained for both brachycephaly and plagiocephaly. The results showed that our method for screening cranial deformities in infants could aid clinical evaluations and parental monitoring of the progression of deformities at home.