protocolSection identificationModule nctId: NCT06366529, orgStudyIdInfo id: TJ-IRB202308123, briefTitle: Explore New Magnetic Resonance Technology in Assessment of Renal Dysfunction, statusModule overallStatus: RECRUITING, startDateStruct date: 2023-09-01, primaryCompletionDateStruct date: 2030-09, completionDateStruct date: 2030-09, studyFirstPostDateStruct date: 2024-04-16, lastUpdatePostDateStruct date: 2024-04-17, sponsorCollaboratorsModule leadSponsor name: Zhen Li, class: OTHER, descriptionModule briefSummary: Currently, renal biopsy is the gold standard for evaluating renal pathology and renal fibrosis, but it is invasive and carries the risk of serious complications; and the sampled tissue is only a small part of the kidney, which is prone to sampling bias. The lack of reliable, comprehensive test results has hindered the research of new anti-fibrotic drugs and delayed the clinical application of effective new drugs. Therefore, the development of a non-invasive dynamic detection method for renal insufficiency and renal fibrosis in vivo is an urgent clinical problem to be solved.With the continuous development and update of technology, imaging provides a new way to non-invasively evaluate renal fibrosis. Due to the high resolution of soft tissue and the ability to perform multi-parameter analysis, magnetic resonance has developed the diagnosis of renal insufficiency and renal fibrosis from macroscopic simple biomorphological changes to microscopically complex pathophysiological changes. Many imaging techniques measure renal dysfunction and renal fibrosis by assessing the impact of fibrosis on the functional status, physical properties, and molecular properties of the kidney.In recent years, in the context of precision medicine, artificial intelligence technologies such as radiomics and machine learning are rapidly becoming very promising auxiliary tools in the imaging assessment of renal fibrosis. It can extract and learn features in images with high throughput, make greater use of information in medical images that cannot be recognized by the human eye, and achieve disease diagnosis, prognosis assessment, and efficacy prediction by building models. However, most of the current research is in the preliminary stage, and there are still few studies on the assessment of renal insufficiency and renal fibrosis. I believe that with the continuous improvement of algorithms and the optimization of models, the progress of radiomics and machine learning will be great. To a certain extent, it promotes the development of personalized medicine and precision medicine for patients with renal insufficiency and renal fibrosis., conditionsModule conditions: Renal Insufficiency, Chronic, designModule studyType: OBSERVATIONAL, designInfo observationalModel: COHORT, timePerspective: PROSPECTIVE, enrollmentInfo count: 500, type: ESTIMATED, outcomesModule primaryOutcomes measure: ESKD, eligibilityModule sex: ALL, stdAges: CHILD, stdAges: ADULT, stdAges: OLDER_ADULT, contactsLocationsModule locations facility: Tongji hospital, NO.1095 jiefang avenue, status: RECRUITING, city: Wuhan, state: Hubei, zip: 430074, country: China, contacts name: Zhen Li, Doctor, role: CONTACT, email: zhenli@hust.edu.cn, geoPoint lat: 30.58333, lon: 114.26667, hasResults: False