White light cystoscopy is key to inform care of patients with suspected or confirmed bladder cancer. Although three-dimensional reconstructions of cystoscopy videos can facilitate rapid, comprehensive review, they are limited by the quality of the original video. Here we address a fundamental bottleneck to reconstruction quality: real-time assessment of frame quality for eventual clinician guidance. We implemented nine metrics and combined them with a random forest classifier that achieves a sensitivity and specificity of 90.6% and 93.7%, respectively. We will use this classifier to perform real-time clinician guidance to facilitate acquisition of high-quality cystoscopy videos that produce robust three-dimensional reconstructions.
Over the past few years, the use of the off-the-shelf video game platform as a rehabilitation tool has gained much interest in physiotherapy. In this paper, we describe an avenue for the integration of virtual reality (VR) and artificial intelligence (AI) based game tracking techniques applied for the purposes of improving the effectiveness of home-care hand physical therapy. We provide an overview of the software and hardware implementation of the prototype based on a LEAP motion sensor input device, which provides two 850nm wavelength infrared (IR) tracking cameras, and an Oculus virtual reality headset. In this initial study, an interactive game is developed on the Unity VR gaming platform that dynamically adjusts the levels of the game to the player performance based on adaptive hand gesture tracking and analysis Al algorithms. A preliminary game evaluation study is conducted on a human subject that showcases the efficiency of the proposed method.
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