Comparison of Analysis Techniques for Assessing Oro-Pharyngeal Swallow from Videofluoroscopy

MBSImP® is an ordinal rating scale designed to evaluate 17 swallowing events from Videofluoroscopic Swallowing Study. Use of an ordinal scale to judge swallowing impairment involves subjectivity and could affect the reliability of judgements. There is a need to validate the ordinal levels of ratings in MBSImP® with objective data, in order to improve confidence of clinical judgements. The hypothesis was that discrete objective data could be obtained for each level of rating in MBSImP® that are statistically different from the data of the subsequent rating level, which would objectively support the concept of the MBSImP® tool. Two hundred 5ml thin liquids swallows were analyzed and each swallow was rated for MBSImP® Component 9- Anterior Hyoid Excursion. As the corresponding objective measure, the anterior excursion of the hyoid in normalized scalar units was measured for each swallow using ImageJ. Statistical analysis of the data with a one way ANOVA revealed a statistically significant difference (p\u3c0.001) in the mean of anterior hyoid excursion in normalized scalar units among the MBSImP® ratings levels with R2 value of 0.20. Multiple paired comparisons performed using Bonferroni adjustment in SPSS revealed significant differences among all ratings levels. The study aimed to find if quantifiable data could be applied to different levels ratings of MBSImP® components. As expected, there was a decrease in the mean anterior hyoid excursion in normalized scalar units as the level of MBSImP® rating increased for Component 9. However, the R2 value of the ANOVA revealed that only 20% of the variation in the objective data of anterior hyoid excursion in normalized scalar units could be explained by different levels of rating on the component of interest of MBSImP® tool. Though this study could not satisfactorily prove the concept of the tool, the objective data of anterior hyoid excursion in normalized scalar units categorized by rating levels of MBSImP® show the potential to achieve this in the future

Use of clinical scores in young Australian adults for prediction of atherosclerosis in middle age

We sought to apply a simple cardiovascular health tool not requiring laboratory tests (the Fuster-BEWAT score, FBS) to predict subclinical atherosclerosis. This study included 2657 young adults (</p

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Suture manipulation post-trabeculectomy: A practical guide

Background: Trabeculectomy is the gold standard filtration surgery for diverting aqueous from anterior chamber to the subconjunctival space. More than the surgery, postoperative follow-ups and management of the blebs play a critical role in the long-term success. This video is aimed at showing the real-world management of blebs postoperatively. Purpose: This video will serve as a practical guide to the postoperative management of trabeculectomy blebs with specific focus on the suture manipulation. Synopsis: This video will demonstrate various suturing techniques of trabeculectomy and their manipulation in the postoperative period. Complications related to each will be discussed. Highlights: We demonstrate how to place and remove, releasable, and fixed sutures. We also address the practical points on why and when to remove the sutures. Suture-related complications and their management have been shown along with practical examples. Video Link: https://youtu.be/2WFQJAPyOv

Long-term exposure to ambient air pollution is associated with coronary artery calcification among asymptomatic adults

We investigated the effects of exposure to very low levels of particulate matte