A Deep Learning Framework for Real‐Time 3D Model Registration in Robot‐Assisted Laparoscopic Surgery

xlomafota13 shared this article with you from Inoreader A Deep Learning Framework for Real‐Time 3D Model Registration in Robot‐Assisted Laparoscopic Surgery Via The International Journal of Medical Robotics and Computer Assisted Surgery Abstract Introduction The current study presents a Deep Learning framework to determine, in real-time, position and rotation of a target organ from an endoscopic video. These inferred data are used to overlay the 3D model of patient's organ over its real counterpart. The resulting augmented video flow is streamed back to the surgeon as a support during laparoscopic Robot-Assisted procedures. Methods This framework exploits semantic segmentation and, thereafter, two techniques, based on Convolutional Neural Networks and motion analysis, were used to infer the rotation. Results The segmentation shows optimal accuracies, with a mean IoU score greater than 80% in all tests. Different performance levels are obtained for rotation, depending on the surgical procedure. Discussion Even if the presented methodology has various degrees of precision depending on the testing scenario, this work sets the first step for the adoption of Deep Learning and Augmented Reality to generalize the automatic registration process. This article is protected by copyright. All rights reserved. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.

A Novel 3-Step Tuning Fork Hearing Test; Preliminary Report on Its Clinical Utility

xlomafota13 shared this article with you from Inoreader A Novel 3-Step Tuning Fork Hearing Test; Preliminary Report on Its Clinical Utility Via Otolaryngology Indian Abstract It is pertinent to have a Tuning Fork Hearing Test that stand-alone can detect severities (Mild, Moderate, Severe and Profound) and the types of hearing losses (Conductive, Sensorineural, and Mixed). A novel 3-Step Tuning Fork Hearing Test (3-STFHT) was attempted for the first time that could detect both the types and the severities of hearing losses. The study was aimed to describe the method of the 3-STFHT and evaluate its clinical utility and reliability. Research Design: Hospital-based observational study of a diagnostic tool. Settings: Otorhinolaryngology Department of a tertiary care medical college hospital. Subjects: 108 adult patients (216 ears) who required hearing evaluation. Main Outcome measures: Sensitivity and specificity of novel 3-STFHT were assessed by comparing its results with the reports of pure tone audiometry in detecting the type and severity of hearing loss. The new 3-STFHT was found very effective (100% sensitivity and specificity) in de tecting conductive and profound sensorineural hearing losses. The sensitivity in detecting sensorineural hearing loss was found 97%-100%. The sensitivity was observed relatively low (92%) at detecting mixed hearing loss. The overall sensitivity and specificity of the 3-STFHT in detecting the types of hearing losses was found 97% and 86% respectively. The novel 3-STFHT, which is simple and convenient, was found very effective in detecting the types and severity of hearing losses. The 3-STFHT can be an important tool in otorhinolaryngology practice and in primary care setting for detecting and screening the types and severities of hearing losses. View on the web Inoreader. Take back control of your news feed. Follow us on Twitter and Facebook.