ArticleMayo Clinic proceedings2023
Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance-Derived Measurement of Total Kidney Volume.
Article in Mayo Clinic proceedings, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- State of the AI: Post-Deployment Monitoring of Radiology-Focused Internally Developed AI.Mayo Clinic proceedings. Digital health · 2026Review
- 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
- Foam sclerotherapy for symptomatic cysts in ADPKD, ADPLD and solitary cysts.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2025Observational
- Reporting ADPK Disease Phenotypes on Abdominal Scans.Kidney international reports · 2025Article
- Intergenerational Enlargement of Human Organs as a Driver of Increased Cancer Risk?BioEssays : news and reviews in molecular, cellular and developmental biology · 2025Review
- Impact of tumor size and sex on diagnostic discrepancies in renal mass imaging.Scientific reports · 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
- Artificial Intelligence in Nephrology: Clinical Applications and Challenges.Kidney medicine · 2025Review
- International Nephrology Masterclass in Chronic Kidney Disease: Rationale, Summary, and Future Perspectives.Life (Basel, Switzerland) · 2024Review
- Effects of artificial intelligence implementation on efficiency in medical imaging-a systematic literature review and meta-analysis.NPJ digital medicine · 2024Review
- MRI of kidney size matters.Magma (New York, N.Y.) · 2024Review
- Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting.Scientific reports · 2024Article
- A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression.Biomedicines · 2024Review
- Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning.Academic radiology · 2024Article
- Artificial intelligence: a new field of knowledge for nephrologists?Clinical kidney journal · 2023Article
- Calculation of Kidney Volumes with Magnetic Resonance in Patients with Autosomal Dominant Polycystic Kidney Disease: Comparison between Methods.Diagnostics (Basel, Switzerland) · 2023Article
- Abdominal Imaging in ADPKD: Beyond Total Kidney Volume.Journal of clinical medicine · 2023Review
- Clinical Quality Control of MRI Total Kidney Volume Measurements in Autosomal Dominant Polycystic Kidney Disease.Tomography (Ann Arbor, Mich.) · 2023Article
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Authors and funding
22 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveTo evaluate the performance of an internally developed and previously validated artificial intelligence (AI) algorithm for magnetic resonance (MR)-derived total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) when implemented in clinical practice. PATIENTS AND
methodsThe study included adult patients with ADPKD seen by a nephrologist at our institution between November 2019 and January 2021 and undergoing an MR imaging examination as part of standard clinical care. Thirty-three nephrologists ordered MR imaging, requesting AI-based TKV calculation for 170 cases in these 161 unique patients. We tracked implementation and performance of the algorithm over 1 year. A radiologist and a radiology technologist reviewed all cases (N=170) for quality and accuracy. Manual editing of algorithm output occurred at radiology or radiology technologist discretion. Performance was assessed by comparing AI-based and manually edited segmentations via measures of similarity and dissimilarity to ensure expected performance. We analyzed ADPKD severity class assignment of algorithm-derived vs manually edited TKV to assess impact.
resultsClinical implementation was successful. Artificial intelligence algorithm-based segmentation showed high levels of agreement and was noninferior to interobserver variability and other methods for determining TKV. Of manually edited cases (n=84), the AI-algorithm TKV output showed a small mean volume difference of -3.3%. Agreement for disease class between AI-based and manually edited segmentation was high (five cases differed).
conclusionPerformance of an AI algorithm in real-life clinical practice can be preserved if there is careful development and validation and if the implementation environment closely matches the development conditions.
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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.