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2026
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| Online Access: | https://doi.org/10.5281/zenodo.19658529 |
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| _version_ | 1866901386358161408 |
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| author | Jakhongir Karshiboev |
| author_facet | Jakhongir Karshiboev |
| contents | <p><em><span>The integration of digital agents into clinical environments represents a paradigm shift in medical diagnostics, directly addressing systemic challenges such as specialist shortages and cognitive fatigue. This paper presents a comprehensive diagnostic ecosystem designed for chest radiography (CXR) analysis, employing a dual-stage deep learning architecture. Utilizing a dataset exceeding 50,000 anonymized images, the system pipeline first validates morphological integrity via YOLOv8, followed by a multi-class pathology classification (Normal, COVID-19, Viral Pneumonia, Lung Opacity) using Convolutional Neural Networks. To bridge the trust deficit often associated with automated systems, spatial heat-mapping via Grad-CAM was implemented, providing visual anatomical rationales for algorithmic determinations. Empirical evaluations demonstrate an aggregate diagnostic accuracy of over 92%, compressing the analytical timeframes from an average of 12 minutes per patient to roughly 5 seconds. The resultant ecosystem, highly accessible via a unified Django web dashboard and cross-platform Telegram bot infrastructure, serves not merely as a clinical triage tool but as a continuous pedagogical instrument, refining the observational heuristics of diagnostic professionals in real-time.</span></em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19658529 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | HUMAN-DIGITAL AGENT SYMBIOSIS IN HEALTHCARE: ACCELERATING DIAGNOSTIC COMPETENCY THROUGH DEEP LEARNING MODALITIES IN CHEST RADIOGRAPHY Jakhongir Karshiboev <p><em><span>The integration of digital agents into clinical environments represents a paradigm shift in medical diagnostics, directly addressing systemic challenges such as specialist shortages and cognitive fatigue. This paper presents a comprehensive diagnostic ecosystem designed for chest radiography (CXR) analysis, employing a dual-stage deep learning architecture. Utilizing a dataset exceeding 50,000 anonymized images, the system pipeline first validates morphological integrity via YOLOv8, followed by a multi-class pathology classification (Normal, COVID-19, Viral Pneumonia, Lung Opacity) using Convolutional Neural Networks. To bridge the trust deficit often associated with automated systems, spatial heat-mapping via Grad-CAM was implemented, providing visual anatomical rationales for algorithmic determinations. Empirical evaluations demonstrate an aggregate diagnostic accuracy of over 92%, compressing the analytical timeframes from an average of 12 minutes per patient to roughly 5 seconds. The resultant ecosystem, highly accessible via a unified Django web dashboard and cross-platform Telegram bot infrastructure, serves not merely as a clinical triage tool but as a continuous pedagogical instrument, refining the observational heuristics of diagnostic professionals in real-time.</span></em></p> |
| title | HUMAN-DIGITAL AGENT SYMBIOSIS IN HEALTHCARE: ACCELERATING DIAGNOSTIC COMPETENCY THROUGH DEEP LEARNING MODALITIES IN CHEST RADIOGRAPHY |
| url | https://doi.org/10.5281/zenodo.19658529 |