ABSTRACT
MediaPipe Hand (MediaPipe) is an artificial intelligence (AI)-based pose estimation library. In this study, MediaPipe was combined with four machine learning (ML) models to estimate the rotation angle of the thumb. Videos of the right hands of 15 healthy volunteers were recorded and processed into 9000 images. The rotation angle of the thumb (defined as angle θ from the palmar plane, which is defined as 0°) was measured using an angle measuring device, expressed in a radian system. Angle θ was then estimated by the ML model by using parameters calculated from the hand coordinates detected by MediaPipe. The linear regression model showed a root mean square error (RMSE) of 12.23, a mean absolute error (MAE) of 9.9, and a correlation coefficient of 0.91. The ElasticNet model showed an RMSE of 12.23, an MAE of 9.95, and a correlation coefficient of 0.91; the support vector machine (SVM) model showed an RMSE of 4.7, an MAE of 2.5, and a correlation coefficient of 0.99. The LightGBM model achieved high values: an RMSE of 4.58, an MAE of 2.62, and a correlation coefficient of 0.99. Based on these findings, we concluded that the thumb rotation angle can be estimated with high accuracy by combining MediaPipe and ML.
ABSTRACT
Traditionally, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. In this study, we attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI) and a machine learning model. Videos of the right shoulder of 10 healthy volunteers (10 males, mean age 37.7 years, mean height 168.3 cm, mean weight 72.7 kg, mean BMI 25.6) were recorded and processed into 10,608 images. Parameters were created using the coordinates measured from the posture estimation AI, and these were used to train the machine learning model. The measured values from the smartphone's angle device were used as the true values to create a machine learning model. When measuring the parameters at each angle, we compared the performance of the machine learning model using both linear regression and Light GBM. When the pose estimation AI was trained using linear regression, a correlation coefficient of 0.971 was achieved, with a mean absolute error (MAE) of 5.778. When trained with Light GBM, the correlation coefficient was 0.999 and the MAE was 0.945. This method enables the estimation of internal and external rotation angles from a direct-facing position. This approach is considered to be valuable for analyzing motor movements during sports and rehabilitation.
Subject(s)
Artificial Intelligence , Machine Learning , Range of Motion, Articular , Shoulder Joint , Humans , Male , Adult , Shoulder Joint/physiology , Range of Motion, Articular/physiology , Female , Rotation , Posture/physiology , Computers, HandheldABSTRACT
Proximal-type epithelioid sarcoma is an aggressive malignant soft-tissue neoplasm, a "proximal" variant of epithelioid sarcoma, resistant to multimodal therapy and involved in early tumor-related death. Pertinent treatments are, therefore, continually being explored. A 24-year-old woman with nonmetastatic proximal-type epithelioid sarcoma, originating subcutaneously on the right side of the vulva, underwent surgical resection; the lesion recurred, however, leading to death 3 months after the second surgery. Here described is a case of proximal-type epithelioid sarcoma expressing L-type amino acid transporter 1 (LAT1) that transports essential amino acids and p-borono-L-phenylalanine (BPA)-the chemical compound used in boron neutron capture therapy (BNCT)-and is highly expressed in many malignant tumors. Recently, LAT1 has drawn attention, and relevant treatments have been studied-LAT1 inhibitor and BNCT. LAT1 expression in proximal-type epithelioid sarcoma may lead to cogent treatments for the disease.
