Publications by authors named "Tomoichi Takahashi"
In this study, we elucidate if synthetic contrast enhanced computed tomography images created from plain computed tomography images using deep neural networks could be used for screening, clinical diagnosis, and postoperative follow-up of small-diameter renal tumors. This retrospective, multicenter study included 155 patients (artificial intelligence training cohort [n = 99], validation cohort [n = 56]) who underwent surgery for small-diameter (≤40 mm) renal tumors, with the pathological diagnosis of renal cell carcinoma, during 2010-2020. We created a learned deep neural networks using pix2pix.
View Article and Find Full Text PDFObjectives: To elucidate the characteristics of uroflowmetry (UFM) observed in men with detrusor underactivity (DU) using our developed artificial intelligence (AI) diagnostic algorithm to distinguish between DU and bladder outlet obstruction (BOO).
Methods: Subjective and objective parameters, including four UFM parameters (first peak flow rate, time to first peak, gradient to first peak, and the ratio of first peak flow rate to maximum flow rate [Q ]) selected by analyzing the judgment basis of the AI diagnostic system, were compared in 266 treatment-naive men with lower urinary tract symptoms (LUTS). Patients were divided into the DU (70; 26.
Objectives: To establish an artificial intelligence diagnostic system for lower urinary tract function in men with lower urinary tract symptoms using only uroflowmetry data and to evaluate its usefulness.
Methods: Uroflowmetry data of 256 treatment-naive men with detrusor underactivity, bladder outlet obstruction, or detrusor underactivity + bladder outlet obstruction were used for artificial intelligence learning and validation using neural networks. An optimal artificial intelligence diagnostic model was established using 10-fold stratified cross-validation and data augmentation.