Enhancing Podocyte Degenerative Changes Identification With Pathologist Collaboration: Implications for Improved Diagnosis in Kidney Diseases

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL IEEE Journal of Translational Engineering in Health and Medicine-Jtehm Pub Date : 2024-09-10 DOI:10.1109/JTEHM.2024.3455941
George Oliveira Barros;José Nathan Andrade Muller da Silva;Henrique Machado de Sousa Proença;Stanley Almeida Araújo;David Campos Wanderley;Luciano Rebouças de Oliveira;Washington Luis Conrado Dos-Santos;Angelo Amancio Duarte;Flavio de Barros Vidal
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Abstract

Podocyte degenerative changes are common in various kidney diseases, and their accurate identification is crucial for pathologists to diagnose and treat such conditions. However, this can be a difficult task, and previous attempts to automate the identification of podocytes have not been entirely successful. To address this issue, this study proposes a novel approach that combines pathologists’ expertise with an automated classifier to enhance the identification of podocytopathies. The study involved building a new dataset of renal glomeruli images, some with and others without podocyte degenerative changes, and developing a convolutional neural network (CNN) based classifier. The results showed that our automated classifier achieved an impressive 90.9% f-score. When the pathologists used as an auxiliary tool to classify a second set of images, the medical group’s average performance increased significantly, from $91.4\pm 12.5$ % to $96.1\pm 2.9$ % of f-score. Fleiss’ kappa agreement among the pathologists also increased from 0.59 to 0.83. Conclusion: These findings suggest that automating this task can bring benefits for pathologists to correctly identify images of glomeruli with podocyte degeneration, leading to improved individual accuracy while raising agreement in diagnosing podocytopathies. This approach could have significant implications for the diagnosis and treatment of kidney diseases. Clinical impact: The approach presented in this study has the potential to enhance the accuracy of medical diagnoses for detecting podocyte abnormalities in glomeruli, which serve as biomarkers for various glomerular diseases.
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与病理学家合作加强荚膜细胞退行性变化的鉴定:改进肾脏疾病诊断的意义
荚膜细胞退行性变化在各种肾脏疾病中很常见,准确识别荚膜细胞对病理学家诊断和治疗此类疾病至关重要。然而,这可能是一项艰巨的任务,以往尝试自动识别荚膜细胞的工作并不完全成功。为了解决这个问题,本研究提出了一种新方法,将病理学家的专业知识与自动分类器相结合,以提高荚膜病变的识别能力。这项研究包括建立一个新的肾小球图像数据集,其中一些有荚膜细胞退行性变化,另一些则没有,并开发了一个基于卷积神经网络(CNN)的分类器。结果显示,我们的自动分类器达到了令人印象深刻的 90.9% f-score。当病理学家使用辅助工具对第二组图像进行分类时,医疗小组的平均成绩显著提高,f-score 从 91.4% 提高到 96.1% 。病理学家之间的 Fleiss' kappa 一致性也从 0.59 提高到了 0.83。结论:这些研究结果表明,这项任务的自动化可以为病理学家正确识别荚膜细胞变性的肾小球图像带来益处,从而提高个人的准确性,同时提高诊断荚膜细胞病变的一致性。这种方法可对肾脏疾病的诊断和治疗产生重大影响。临床影响:本研究提出的方法有望提高医学诊断检测肾小球荚膜异常的准确性,而荚膜异常是各种肾小球疾病的生物标志物。
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来源期刊
CiteScore
7.40
自引率
2.90%
发文量
65
审稿时长
27 weeks
期刊介绍: The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.
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