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Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.
JMIR Form Res
January 2025
Department of Psychology, The University of Texas at San Antonio, San Antonio, TX, United States.
Background: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns.
View Article and Find Full Text PDFCombining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study.
J Med Internet Res
January 2025
Univ Rennes, CHU Rennes, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.
Background: To reduce the mortality related to bladder cancer, efforts need to be concentrated on early detection of the disease for more effective therapeutic intervention. Strong risk factors (eg, smoking status, age, professional exposure) have been identified, and some diagnostic tools (eg, by way of cystoscopy) have been proposed. However, to date, no fully satisfactory (noninvasive, inexpensive, high-performance) solution for widespread deployment has been proposed.
View Article and Find Full Text PDFAI enabled, mobile soil pH classification with colorimetric paper sensors for sustainable agriculture.
PLoS One
January 2025
IBM Research, Rio de Janeiro, Brazil.
For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits from real-time, on-the-spot chemical analysis of soil at low cost. Colorimetric paper sensors are ideal candidates, however, their automated readout and analysis in the field is needed. Using mobile technology for paper sensor readout could, in principle, enable the application of machine-learning models for transforming colorimetric data into threshold-based classes that represent chemical concentration.
View Article and Find Full Text PDFCitrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic.
PLoS One
January 2025
Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Republic of Korea.
Citrus farming is one of the major agricultural sectors of Pakistan and currently represents almost 30% of total fruit production, with its highest concentration in Punjab. Although economically important, citrus crops like sweet orange, grapefruit, lemon, and mandarins face various diseases like canker, scab, and black spot, which lower fruit quality and yield. Traditional manual disease diagnosis is not only slow, less accurate, and expensive but also relies heavily on expert intervention.
View Article and Find Full Text PDFCell fate decisions, such as proliferation, differentiation, and death, are driven by complex molecular interactions and signaling cascades. While significant progress has been made in understanding the molecular determinants of these processes, historically, cell fate transitions were identified through light microscopy that focused on changes in cell morphology and function. Modern techniques have shifted towards probing molecular effectors to quantify these transitions, offering more precise quantification and mechanistic understanding.
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