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Application of cloud server-based machine learning for assisting pathological structure recognition in IgA nephropathy.
Huang, Yu-Lin; Liu, Xiao Qi; Huang, Yang; Jin, Feng Yong; Zhao, Qing; Wu, Qin Yi; Ma, Kun Ling.
Afiliación
  • Huang YL; Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
  • Liu XQ; Department of Nephrology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
  • Huang Y; Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
  • Jin FY; Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
  • Zhao Q; Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
  • Wu QY; Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
  • Ma KL; Department of Nephrology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China klma@zju.edu.cn.
J Clin Pathol ; 2023 Dec 18.
Article en En | MEDLINE | ID: mdl-38123970
ABSTRACT

BACKGROUND:

Machine learning (ML) models can help assisting diagnosis by rapidly localising and classifying regions of interest (ROIs) within whole slide images (WSIs). Effective ML models for clinical decision support require a substantial dataset of 'real' data, and in reality, it should be robust, user-friendly and universally applicable.

METHODS:

WSIs of primary IgAN were collected and annotated. The H-AI-L algorithm which could facilitate direct WSI viewing and potential ROI detection for clinicians was built on the cloud server of matpool, a shared internet-based service platform. Model performance was evaluated using F1-score, precision, recall and Matthew's correlation coefficient (MCC).

RESULTS:

The F1-score of glomerular localisation in WSIs was 0.85 and 0.89 for the initial and pretrained models, respectively, with corresponding recall values of 0.79 and 0.83, and precision scores of 0.92 and 0.97. Dichotomous differentiation between global sclerotic (GS) and other glomeruli revealed F1-scores of 0.70 and 0.91, and MCC values of 0.55 and 0.87, for the initial and pretrained models, respectively. The overall F1-score of multiclassification was 0.81 for the pretrained models. The total glomerular recall rate was 0.96, with F1-scores of 0.68, 0.56 and 0.26 for GS, segmental glomerulosclerosis and crescent (C), respectively. Interstitial fibrosis/tubular atrophy lesion similarity between the true label and model predictions was 0.75.

CONCLUSIONS:

Our results underscore the efficacy of the ML integration algorithm in segmenting ROIs in IgAN WSIs, and the internet-based model deployment is in favour of widespread adoption and utilisation across multiple centres and increased volumes of WSIs.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Pathol Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Pathol Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido