ArticleInvestigative and clinical urology2020
Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver.
Article in Investigative and clinical urology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.Journal of medical systems · 2024Pooled it
- A rapid automated segmentation method for total kidney volume measurement on unenhanced computed tomography in autosomal dominant polycystic kidney disease.Abdominal radiology (New York) · 2026Article
- Automatically Measuring Kidney, Liver, and Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease.Journal of the American Society of Nephrology : JASN · 2026Article
- Are Image-Based Deep Learning Algorithms of Kidney Volume in Polycystic Kidney Disease Ready for Clinical Deployment? A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Association between induced organ atrophy assessed by artificial intelligence-generated automatic segmentation and efficacy of bevacizumab in combination with chemotherapy in metastatic colorectal cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Reporting ADPK Disease Phenotypes on Abdominal Scans.Kidney international reports · 2025Article
- Automatic head and neck tumor segmentation through deep learning and Bayesian optimization on three-dimensional medical images.Computers in biology and medicine · 2025Article
- The Role of Baseline Total Kidney Volume Growth Rate in Predicting Tolvaptan Efficacy for ADPKD Patients: A Feasibility Study.Journal of clinical medicine · 2025Article
- Segmentation of ADPKD Computed Tomography Images with Deep Learning Approach for Predicting Total Kidney Volume.Biomedicines · 2025Article
- Multiscale and multimodal evaluation of autosomal dominant polycystic kidney disease development.Communications biology · 2024Article
- Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning.Academic radiology · 2024Article
- AI models for automated segmentation of engineered polycystic kidney tubules.Scientific reports · 2024Article
- CT-based radiomics: predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis.Visual computing for industry, biomedicine, and art · 2024Article
- Artificial Intelligence-powered automatic volume calculation in medical images - available tools, performance and challenges for nuclear medicine.Nuklearmedizin. Nuclear medicine · 2023Review
- Non-contrast low-dose CT can be used for volumetry of ADPKD.BMC nephrology · 2023Article
- Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease.Investigative and clinical urology · 2023Article
- Review
- Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease.Tomography (Ann Arbor, Mich.) · 2022Article
- Polycystic liver: automatic segmentation using deep learning on CT is faster and as accurate compared to manual segmentation.European radiology · 2022Article
- Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements.Abdominal radiology (New York) · 2022Article
Corrections and comments
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Authors and funding
18 authors at 9 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeVolumetry is used in polycystic kidney and liver diseases (PKLDs), including autosomal dominant polycystic kidney disease (ADPKD), to assess disease progression and drug efficiency. However, since no rapid and accurate method for volumetry has been developed, volumetry has not yet been established in clinical practice, hindering the development of therapies for PKLD. This study presents an artificial intelligence (AI)-based volumetry method for PKLD. MATERIALS AND
methodsThe performance of AI was first evaluated in comparison with ground-truth (GT). We trained a V-net-based convolutional neural network on 175 ADPKD computed tomography (CT) segmentations, which served as the GT and were agreed upon by 3 experts using images from 214 patients analyzed with volumetry. The dice similarity coefficient (DSC), interobserver correlation coefficient (ICC), and Bland-Altman plots of 39 GT and AI segmentations in the validation set were compared. Next, the performance of AI on the segmentation of 50 random CT images was compared with that of 11 PKLD specialists based on the resulting DSC and ICC.
resultsThe DSC and ICC of the AI were 0.961 and 0.999729, respectively. The error rate was within 3% for approximately 95% of the CT scans (error<1%, 46.2%; 1%≤error<3%, 48.7%). Compared with the specialists, AI showed moderate performance. Furthermore, an outlier in our results confirmed that even PKLD specialists can make mistakes in volumetry.
conclusionsPKLD volumetry using AI was fast and accurate. AI performed comparably to human specialists, suggesting its use may be practical in clinical settings.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.