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Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research.
Ophthalmol Ther
January 2025
International Health Policy Program (IHPP), Ministry of Public Health, Nonthaburi, Thailand.
Introduction: Screening diabetic retinopathy (DR) for timely management can reduce global blindness. Many existing DR screening programs worldwide are non-digital, standalone, and deployed with grading retinal photographs by trained personnel. To integrate the screening programs, with or without artificial intelligence (AI), into hospital information systems to improve their effectiveness, the non-digital workflow must be transformed into digital.
View Article and Find Full Text PDFImproving Real-Time Feedback During Cochlear Implantation: The Auditory Nerve Neurophonic/Cochlear Microphonic Ratio.
Ear Hear
January 2025
Department of Otorhinolaryngology and Head and Neck Surgery, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
Objectives: Real-time monitoring of cochlear function to predict the loss of residual hearing after cochlear implantation is now possible. Current approaches monitor the cochlear microphonic (CM) during implantation from the electrode at the tip of the implant. A drop in CM response of >30% is associated with poorer hearing outcomes.
View Article and Find Full Text PDFAccurate colorectal cancer detection using a random hinge exponential distribution coupled attention network on pathological images.
Abdom Radiol (NY)
January 2025
Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, India.
Colorectal cancer (CRC) is one of the most common and deadly forms of cancer worldwide, necessitating accurate and early detection to improve treatment outcomes. Traditional diagnostic methods often rely on manual examination of pathological images, which can be time-consuming and prone to human error. This study presents an advanced approach for colorectal cancer detection using a Random Hinge Exponential Distribution coupled Attention Network (RHED-CANet) on pathological images.
View Article and Find Full Text PDFOpen-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments.
Sci Adv
January 2025
Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
Pathogenic bioaerosols are critical for outbreaks of airborne disease; however, rapidly and accurately identifying pathogens directly from complex air environments remains highly challenging. We present an advanced method that combines open-set deep learning (OSDL) with single-cell Raman spectroscopy to identify pathogens in real-world air containing diverse unknown indigenous bacteria that cannot be fully included in training sets. To test and further enhance identification, we constructed the Raman datasets of aerosolized bacteria.
View Article and Find Full Text PDFPerformance of Ultrasensitive Polymerase Chain Reaction Testing for JC Polyomavirus in Cerebrospinal Fluid Compared with Pathological Diagnosis of Progressive Multifocal Leukoencephalopathy.
Viruses
December 2024
Department of Pathology, National Institute of Infectious Diseases, Shinjuku-ku, Tokyo 162-8640, Japan.
Progressive multifocal leukoencephalopathy (PML) is a demyelinating disease caused by the JC polyomavirus (JCPyV). Based on the clinical criteria, PML is diagnosed via polymerase chain reaction (PCR) detection of JCPyV DNA in cerebrospinal fluid (CSF) in combination with neurological and imaging findings. Although the utility of CSF JCPyV testing using ultrasensitive PCR assays has been suggested, its potential requires further evaluation.
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