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

withdrawn 2017 hrs ehra ecas aphrs solaece expert consensus statement on catheter and surgical ablation of atrial fibrillation

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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

Exploring experiences of living with removable dentures – a scoping review of qualitative literature

Objective Examine the literature on the experiences of living with removable dentures (complete or partial) to identify any gaps and provide a map for future research. Background Increasing proportions of society are living partially dentate with some form of restoration, including removable dentures. Previous studies have reported on the location, materials and usage of these prostheses, along with effects on oral-health-related quality of life (OHRQoL). However, less is known about experiences with removable dentures from a patient-centred perspective. Methods A scoping review of the qualitative literature was undertaken using the framework of Arksey and O'Malley, updated by Levac et al. Literature searches were carried out using Medline and Web of Science. Papers were screened by title and abstract using inclusion and exclusion criteria. Remaining papers were read in full and excluded if they did not meet the required criteria. Nine papers were included in the final review. Findings Key themes from these papers were: impact of tooth loss and living without teeth, and its impacts in relation to social position, appearance, confidence and function (chewing and speaking); social norms and tooth loss, including attitudes to tooth retention and treatment costs, and changes in intergenerational norms towards dentures; expectations of treatment, including patients being more involved in decision making, viewing the denture as a “gift” and dentures helping to achieve “an ideal”; living with a removable denture (complete or partial), including patient preparedness for a denture, adaptation and impacts on activities and participation; and the dentist-patient relationship, including issues with information and communication, and differing priorities between patients and dentists. Conclusion Little qualitative research exists on experiences of living with a removable denture. Existing literature demonstrates the importance of dispersed activities in differing social, spatial and temporal contexts when wearing removable dentures. Focusing on processes of positive adaptation to dentures and OHRQoL, rather than deficits, is also required to fully understand patients' experiences. Additionally, more complex technological advances may not always be in the best interest of every patient

On the fatigue and dwell-fatigue behavior of a low-density steel and the correlated microstructure origin of damage mechanism

Abstract The present work deals with revealing the fatigue and dwell-fatigue behavior and correlated damage mechanisms of Fe–Mn–Al–C lightweight steel. Surprisingly, alteration in loading mode from monotonic to cyclic induces reversible dislocation movement and facilitates the occurrence of dynamic strain aging. Additionally, applying dwell time by an acceleration of strain aging intensified stress asymmetry during dwell fatigue. The occurrence of strain aging has a bilateral effect on the crack initiation and growth. On one hand, strain aging stimulates twin formation and retards fatigue crack initiation, however, on the other hand, reduces hardening capacity, restricts the plastic deformation and facilitates crack propagation