The Diagnostic Classification of the Pathological Image Using Computer Vision

Algorithms, Journal Year: 2025, Volume and Issue: 18(2), P. 96 - 96

Published: Feb. 8, 2025

Computer vision and artificial intelligence have revolutionized the field of pathological image analysis, enabling faster more accurate diagnostic classification. Deep learning architectures like convolutional neural networks (CNNs), shown superior performance in tasks such as classification, segmentation, object detection pathology. has significantly improved accuracy disease diagnosis healthcare. By leveraging advanced algorithms machine techniques, computer systems can analyze medical images with high precision, often matching or even surpassing human expert performance. In pathology, deep models been trained on large datasets annotated pathology to perform cancer diagnosis, grading, prognostication. While approaches show great promise challenges remain, including issues related model interpretability, reliability, generalization across diverse patient populations imaging settings.

Language: Английский

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OVision A raspberry Pi powered portable low cost medical device framework for cancer diagnosis

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 28, 2025

Cancer remains a major global health challenge, with significant disparities in access to advanced diagnostic and prognostic technologies, especially resource-constrained settings. Existing medical treatments devices for cancer diagnosis are often prohibitively expensive, limiting their reach impact. Pathologists' scarcity exacerbates accuracy, elevating mortality risks. To address these critical issues, this study presents OVision - low cost, deep learning-powered framework developed assist histopathological diagnosis. The key objective is leverage the portable, low-power computing Raspberry Pi. By designing standalone that eliminate need internet connectivity high-end infrastructure, we can dramatically reduce costs while maintaining accuracy. As proof of concept, demonstrated viability through compact, self-contained device capable accurately detecting ovarian subtypes 95% on par traditional methods, costing small fraction price. This off-grid solution has immense potential improve precision diagnostics, underserved regions world lack resources deploy infrastructure-heavy technologies. In addition, by classifying each tile, tool provide percentages histologic subtype detected within slide. capability enhances precision, offering detailed overview heterogeneity tissue sample, helps understanding complexity tailoring personalized treatment plans. conclusion, work proposes transformative model developing affordable, accessible bring healthcare benefits all, laying foundation more equitable, inclusive future medicine.

Language: Английский

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An artificial intelligence tool that may assist with interpretation of rapid plasma reagin test for syphilis: development and on-site evaluation

Journal of Infection, Journal Year: 2025, Volume and Issue: unknown, P. 106454 - 106454

Published: March 1, 2025

The rapid plasma reagin (RPR) test, a traditional method for diagnosing syphilis and evaluating treatment efficacy, relies on subjective interpretation requires high technical proficiency. This study aimed to develop validate user-friendly RPR-artificial intelligence (AI) interpretative tool. A dataset comprising 600 images of photographed RPR cards from 276 negative 223 positive samples was used model development. reference result based consistent interpretations by at least two out three experienced laboratory personnel. Then an developed using deep learning algorithms loaded into smartphones on-site clinical centers October 2023 April 2024. demonstrated accuracy 82·67% (95% CI 71·82%-90·09%) reactive circles 84·44% 69·94%-93·01%) non-reactive circles. In the field study, 669 specimens showed sensitivity 94·85% 89·29%-97·73%), specificity 91·56% 88·78%-93·71%), concordance 92·23% 89·87%-94·09%). predictive value 74·14% 66·86%-80·33%) 98.59% 96·98%-99·38%). tool assists in standardization, enabling data traceability, quality control remote underdeveloped areas.

Language: Английский

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MobileDenseNeXt: Investigations on biomedical image classification

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124685 - 124685

Published: July 3, 2024

Language: Английский

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Adverse Prognostic Impact of Transitional and Pleomorphic Patterns in Pleural Nonepithelioid Mesothelioma: Insights From Comprehensive Analysis and Reticulin Stain

Archives of Pathology & Laboratory Medicine, Journal Year: 2024, Volume and Issue: unknown

Published: July 2, 2024

Mesothelioma subtyping into epithelioid and nonepithelioid categories plays a crucial role in prognosis treatment selection, with emerging recognition of the impact various histologic patterns.

Language: Английский

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