NCT06690190|Active Not Recruiting
Artificial Intelligence-based Techniques to Characterize KIdney Microstructure on Histological ImagEs
AI-TIME
Loading search results
AI-TIME
Sponsor study identifier
observational
100
1 country
1
Study Start
November 8, 2024
Actual (reported)Primary Completion
November 2034
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Study Completion
November 2034
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
The primary aim of this observational exploratory study will be to use fully anonymized histological images of kidney human tissue from patients with any kidney disease and normal kidney tissue to develop novel deep learning-based image processing techniques allowing to characterize kidney microstructure across different pathologies and/or disease stages. Secondly, the study will aim at validating the novel techniques against gold standard (manual) methods, when available, and at developing novel histological imaging biomarkers that could support differential diagnosis, staging of the disease, monitoring of disease progression and response to therapy, and prediction of the disease progression. Other exploratory aims will include:
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
November 2034
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Click on a node to explore related trials.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
November 8, 2024
Actual (reported)November 13, 2024
Actual (reported)November 15, 2024
Actual (reported)November 2034
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
November 2034
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Image processing techniques
Develop novel deep learning-based image processing techniques allowing to characterize kidney microstructure across different pathologies and/or disease stages.
From image acquisition to study end at 10 years
Patients with any kidney disease
Subjects with normal kidneys
The study will use fully anonymized histological images of both human renal tissue samples from patients with renal disease and healthy renal tissue, acquired in the context of clinical studies promoted by the Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, via optical microscopy, immunofluorescence or electron microscopy, after appropriate staining.
Clinical Research Centre for Rare Diseases Aldo e Cele Daccò
Ranica, BG, 24020, Italy
van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med. 2021 May;27(5):775-784. doi: 10.1038/s41591-021-01343-4. Epub 2021 May 14.
Bouteldja N, Klinkhammer BM, Bulow RD, Droste P, Otten SW, Freifrau von Stillfried S, Moellmann J, Sheehan SM, Korstanje R, Menzel S, Bankhead P, Mietsch M, Drummer C, Lehrke M, Kramann R, Floege J, Boor P, Merhof D. Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology. J Am Soc Nephrol. 2021 Jan;32(1):52-68. doi: 10.1681/ASN.2020050597. Epub 2020 Nov 5.
Fully anonymized histological images of kidney human tissue.
Giuseppe Remuzzi, M.D.
Istituto Di Ricerche Farmacologiche Mario Negri
First Submitted
November 13, 2024
First Posted
November 15, 2024
Study Start
November 8, 2024
Actual (reported)Primary Completion
November 2034
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Contact the study team to confirm eligibility.
Becker JU, Mayerich D, Padmanabhan M, Barratt J, Ernst A, Boor P, Cicalese PA, Mohan C, Nguyen HV, Roysam B. Artificial intelligence and machine learning in nephropathology. Kidney Int. 2020 Jul;98(1):65-75. doi: 10.1016/j.kint.2020.02.027. Epub 2020 Apr 1.
Study Completion
November 2034
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Last Updated
November 15, 2024
Record last verified: 2024-11
Download the complete ClinicalTrials.gov record imported by Syfrah, including fields not shown in this page. The registry may contain a newer revision.
Source: ClinicalTrials.gov.
JSON downloads include source attribution and retrieval details alongside the unchanged original record or the separately normalized values.
Source retrieved: