Dataset Information

Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning.

ABSTRACT: Identification of glomerular lesions and structures is a key point for pathological diagnosis, treatment instructions, and prognosis evaluation in kidney diseases. These time-consuming tasks require a more accurate and reproducible quantitative analysis method. We established derivation and validation cohorts composed of 400 Chinese patients with immunoglobulin A nephropathy (IgAN) retrospectively. Deep convolutional neural networks and biomedical image processing algorithms were implemented to locate glomeruli, identify glomerular lesions (global and segmental glomerular sclerosis, crescent, and none of the above), identify and quantify different intrinsic glomerular cells, and assess a network-based mesangial hypercellularity score in periodic acid-Schiff (PAS)-stained slides. Our framework achieved 93.1% average precision and 94.9% average recall for location of glomeruli, and a total Cohen's kappa of 0.912 [95% confidence interval (CI), 0.892-0.932] for glomerular lesion classification. The evaluation of global, segmental glomerular sclerosis, and crescents achieved Cohen's kappa values of 1.0, 0.776, 0.861, and 95% CI of (1.0, 1.0), (0.727, 0.825), (0.824, 0.898), respectively. The well-designed neural network can identify three kinds of intrinsic glomerular cells with 92.2% accuracy, surpassing the about 5-11% average accuracy of junior pathologists. Statistical interpretation shows that there was a significant difference (P value?

SUBMITTER: Zeng C

PROVIDER: S-EPMC7496925 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning.

Zeng Caihong C Nan Yang Y Xu Feng F Lei Qunjuan Q Li Fengyi F Chen Tingyu T Liang Shaoshan S Hou Xiaoshuai X Lv Bin B Liang Dandan D Luo WeiLi W Lv Chuanfeng C Li Xiang X Xie Guotong G Liu Zhihong Z

The Journal of pathology 20200707 1

Identification of glomerular lesions and structures is a key point for pathological diagnosis, treatment instructions, and prognosis evaluation in kidney diseases. These time-consuming tasks require a more accurate and reproducible quantitative analysis method. We established derivation and validation cohorts composed of 400 Chinese patients with immunoglobulin A nephropathy (IgAN) retrospectively. Deep convolutional neural networks and biomedical image processing algorithms were implemented to ...[more]

PMID: 32542677

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Project description:Background and objectivesSepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.Design, setting, participants, & measurementsWe used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement.ResultsWe identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for K-means clustering, identifying three subphenotypes. Subphenotype 1 had 1443 patients, and subphenotype 2 had 1898 patients, whereas subphenotype 3 had 660 patients. Subphenotype 1 had the lowest proportion of liver disease and lowest Simplified Acute Physiology Score II scores compared with subphenotypes 2 and 3. The proportions of patients with CKD were similar between subphenotypes 1 and 3 (15%) but highest in subphenotype 2 (21%). Subphenotype 1 had lower median bilirubin levels, aspartate aminotransferase, and alanine aminotransferase compared with subphenotypes 2 and 3. Patients in subphenotype 1 also had lower median lactate, lactate dehydrogenase, and white blood cell count than patients in subphenotypes 2 and 3. Subphenotype 1 also had lower creatinine and BUN than subphenotypes 2 and 3. Dialysis requirement was lowest in subphenotype 1 (4% versus 7% [subphenotype 2] versus 26% [subphenotype 3]). The mortality 28 days after AKI was lowest in subphenotype 1 (23% versus 35% [subphenotype 2] versus 49% [subphenotype 3]). After adjustment, the adjusted odds ratio for mortality for subphenotype 3, with subphenotype 1 as a reference, was 1.9 (95% confidence interval, 1.5 to 2.4).ConclusionsUtilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.

Dataset Information

Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning.

Publications

Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning.

Similar Datasets

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