The gait kinematics of an individual is affected by various factors, including age, anthropometry, gender, and disease. Detecting anomalous gait features aids in the diagnosis and treatment of gait-related diseases. The objective of this study was to develop a machine learning method for automatically classifying five anomalous gait features, i.e., toe-out, genu varum, pes planus, hindfoot valgus, and forward head posture features, from threedimensional data on gait kinematics. Gait data and gait feature labels of 488 subjects were acquired. The orientations of the human body segments during a gait cycle were mapped to a low-dimensional latent gait vector using a variational autoencoder. A two-layer neural network was trained to classify five gait features using logistic regression and calculate an anomalous gait feature vector (AGFV). The proposed network showed balanced accuracies of 82.8% for a toe-out, 85.9% for hindfoot valgus, 80.2% for pes planus, 73.2% for genu varum, and 92.9% for forward head posture when the AGFV was rounded to the nearest zero or 1. Multiple anomalous gait features were detectable using the proposed method, which has a practical advantage over current gait indices, including the gait deviation index with a single value. The overall results confirmed the feasibility of using the proposed method for screening subjects with anomalous gait features using three-dimensional motion capture data.

Objectives High‐contrast and high‐resolution imaging techniques would enable real‐time sensitive detection of the gastrointestinal lesions. This study aimed to investigate the feasibility of novel dual fluorescence imaging using moxifloxacin and proflavine in the detection of neoplastic lesions of the human gastrointestinal tract. Methods Patients with the colonic and gastric neoplastic lesions were prospectively enrolled. The lesions were biopsied with forceps or endoscopically resected. Dual fluorescence imaging was performed by using custom axially swept wide‐field fluorescence microscopy after topical moxifloxacin and proflavine instillation. Imaging results were compared with both confocal imaging with cell labeling and conventional histological examination. Results Ten colonic samples (one normal mucosa, nine adenomas) from eight patients and six gastric samples (one normal mucosa, five adenomas) from four patients were evaluated. Dual fluorescence imaging visualized detail cellular structures. Regular glandular structures with polarized cell arrangement were observed in normal mucosa. Goblet cells were preserved in normal colonic mucosa. Irregular glandular structures with scanty cytoplasm and dispersed elongated nuclei were observed in adenomas. Goblet cells were scarce or lost in the colonic lesions. Similarity analysis between moxifloxacin and proflavine imaging showed relatively high correlation values in adenoma compared with those in normal mucosa. Dual fluorescence imaging showed good detection accuracies of 82.3% and 86.0% in the colonic and the gastric lesions, respectively. Conclusions High‐contrast and high‐resolution dual fluorescence imaging was feasible for obtaining detail histopathological information in the gastrointestinal neoplastic lesions. Further studies are needed to develop dual fluorescence imaging as an in vivo real‐time visual diagnostic method.