Fully Automated Longitudinal Assessment of Renal Stone Burden on Serial CT Imaging Using Deep Learning

Mukherjee, Pritam; Lee, Sungwon; Elton, Daniel C.; Nakada, Stephen Y.; Pickhardt, Perry J.; Summers, Ronald M.

doi:10.1089/end.2023.0066

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…Meanwhile, there was a good correlation between the model and manual measurements of stone volume. Recently, on the basis of their previous work, Mukherjee et al proposed a slightly modified pipeline which updated the segmentation method and volume threshold, obtaining a per-scan sensitivity of 97.8% [66]. The renal stone volume and its interval changes on serial CT scans were assessed by the deep learning-based automated measurements and manual measurements respectively, and it also showed good agreement between two kinds of measurements.…”

Section: Detection Of Renal Calculusmentioning

confidence: 99%

Imaging-based deep learning in kidney diseases: recent progress and future prospects

Zhang,

Ye,

Yuan

et al. 2024

Insights Imaging

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Kidney diseases result from various causes, which can generally be divided into neoplastic and non-neoplastic diseases. Deep learning based on medical imaging is an established methodology for further data mining and an evolving field of expertise, which provides the possibility for precise management of kidney diseases. Recently, imaging-based deep learning has been widely applied to many clinical scenarios of kidney diseases including organ segmentation, lesion detection, differential diagnosis, surgical planning, and prognosis prediction, which can provide support for disease diagnosis and management. In this review, we will introduce the basic methodology of imaging-based deep learning and its recent clinical applications in neoplastic and non-neoplastic kidney diseases. Additionally, we further discuss its current challenges and future prospects and conclude that achieving data balance, addressing heterogeneity, and managing data size remain challenges for imaging-based deep learning. Meanwhile, the interpretability of algorithms, ethical risks, and barriers of bias assessment are also issues that require consideration in future development. We hope to provide urologists, nephrologists, and radiologists with clear ideas about imaging-based deep learning and reveal its great potential in clinical practice.Critical relevance statement The wide clinical applications of imaging-based deep learning in kidney diseases can help doctors to diagnose, treat, and manage patients with neoplastic or non-neoplastic renal diseases.Key points• Imaging-based deep learning is widely applied to neoplastic and non-neoplastic renal diseases.• Imaging-based deep learning improves the accuracy of the delineation, diagnosis, and evaluation of kidney diseases.• The small dataset, various lesion sizes, and so on are still challenges for deep learning. Graphical Abstract

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Section: Detection Of Renal Calculusmentioning

confidence: 99%