数字中医药（英文）2025，Vol.8Issue(3)：313-322,10.DOI:10.1016/j.dcmed.2025.09.004
通过人工智能与数字工具复兴尤纳尼医学的经典尿液诊断:迈向传统医学体系的整合信息学
Reviving classical Bawl(urine)diagnostics in Unani medicine via artificial intelligence and digital tools:toward integrative informatics for traditional systems
Farooqui Shazia Parveen 1Khaleel Ahmed 2Athar Parvez Ansari 1Kazi Kabiruddin Ahmed 1Noor Zaheer Ahmed 3Shaheen Akhlaq 1Sendhilkumar Selvaradjou4
作者信息
- 1. Regional Research Institute of Unani Medicine,Central Council for Research in Unani Medicine,Chennai,Tamil Nadu 600013,India
- 2. Department of Paediatrics,Luqman Unani Medical College,Hospital & Research Centre,Vijayapura,Karnataka 586104,India
- 3. Central Council for Research in Unani Medicine,Ministry of Ayush,Government of India,New Delhi,110058,India
- 4. Department of Information Science and Technology,Anna University,Chennai,Tamil Nadu 600025,India
- 折叠
摘要
Abstract
In Unani medicine,Bawl(urine)is recognized as a key diagnostic tool,with humoural imbal-ances assessed via parameters like color,consistency,sediment,clarity,froth,odor,and vol-ume.This conceptual review explores how these classical diagnostic indicators may be con-textualized alongside modern urinalysis markers(e.g.,bilirubin,protein,ketones,and sedi-mentation)and examined through emerging artificial intelligence(AI)frameworks.Potential applications include ResNet-18 for color classification,You Only Look Once version 8(YOLOv8)for sediment detection,long short-term memory(LSTM)for viscosity estimation,and EfficientDet for froth analysis,with standardized urine images/videos forming the basis of future datasets.Additionally,a comparative ontology is proposed to align Unani perspec-tives with diagnostic approaches in traditional Chinese medicine,encouraging cross-system integration.By synthesizing classical epistemology with computational intelligence,this re-view highlights pathways for developing AI-based decision support systems to promote per-sonalized,accessible,and telemedicine-enabled healthcare.关键词
尤纳尼医学/Bawl(尿液)诊断法/人工智能/深度学习/ResNet/YOLOv8/尿液生物标志物Key words
Unani medicine/Bawl(urine)diagnostics/Artificial intelligence/Deep learning/ResNet/YOLOv8/Urine biomarkers引用本文复制引用
Farooqui Shazia Parveen,Khaleel Ahmed,Athar Parvez Ansari,Kazi Kabiruddin Ahmed,Noor Zaheer Ahmed,Shaheen Akhlaq,Sendhilkumar Selvaradjou..通过人工智能与数字工具复兴尤纳尼医学的经典尿液诊断:迈向传统医学体系的整合信息学[J].数字中医药（英文）,2025,8(3):313-322,10.
