Enhancing prosthetic vision by upgrade of a subretinal photovoltaic implant in situ

In patients with atrophic age-related macular degeneration, subretinal photovoltaic implant (PRIMA) provided visual acuity up to 20/440, matching its 100μm pixels size. Next-generation implants with smaller pixels should significantly improve the acuity. This study in rats evaluates removal of a subretinal implant, replacement with a newer device, and the resulting grating acuity in-vivo. Six weeks after the initial implantation with planar and 3-dimensional devices, the retina was re-detached, and the devices were successfully removed. Histology demonstrated a preserved inner nuclear layer. Re-implantation of new devices into the same location demonstrated retinal re-attachment to a new implant. New devices with 22μm pixels increased the grating acuity from the 100μm capability of PRIMA implants to 28μm, reaching the limit of natural resolution in rats. Reimplanted devices exhibited the same stimulation threshold as for the first implantation of the same implants in a control group. This study demonstrates the feasibility of safely upgrading the subretinal photovoltaic implants to improve prosthetic visual acuity

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

Who will teach the teachers: An analysis of the inhaler technique of Indian patients and health care providers in a tertiary health care centre

Introduction: The proper use of inhalers is essential for ensuring proper control of the disease. Various studies have shown high levels of improper use and lack of knowledge of the correct technique among patients with asthma. However, less data are available on how health care workers (HCW′s) use inhalers. Materials and Methods: The study was conducted at a Tertiary Care Hospital in Mumbai. We evaluated the pMDI technique in 141 consecutive adult asthmatics and 100 HCW′s. All patients and HCW′s were graded out of 10 points for following 10 steps. These were derived from Melani et al.′s study on inhaler mishandling. Results: Techniques of 141 patients and 100 HCW′s (55 nurses and 45 doctors) were analyzed. The average technique score among patients ranged from 0 to 10 with a mean of 4.65 ± 2.00. The combined score for health workers ranged from 3 to 9 with a mean of 5.45 ± 1.47. Doctors had a higher score of 6.35 ± 1.33 as opposed to the nurses′ score of 4.70 ± 1.13 (P 0.05). Conclusions: Our study highlights the need for better education of not only patients but also health care providers regarding the appropriate use of inhaler devices in order to achieve optimal control of obstructive airway diseases