Apply Machine Learning to the Interpretation of Urinary Crystal

Status Not yet recruiting

Clinical Trial Summary

The goal of this observational study is to developing an image-based artificial intelligence software that can automatically interpret the types and sizes of crystals in urine. The main question[s] it aims to answer are: - Allowing healthcare professionals to input urine images and receive real-time reading results on crystal types and sizes. - This aims to provide a faster, more objective, and accurate analysis of crystals. We anticipate delivering an image AI software suitable for practical applications, promoting the automation and accuracy of urine crystal analysis.

Clinical Trial Description

Kidney stones are primarily formed due to the supersaturation of ions in urine, leading to the formation of crystals. An assessment of the risk of kidney stones is based on a patient's medical history, biochemical urine tests, and various laboratory examinations. Combining these with imaging studies such as CT scans, ultrasound, and X-rays helps in diagnosing the type of kidney stones, though imaging results for smaller stones may be less accurate. Stone formation is common with a high recurrence rate, and there is a strong correlation between urine crystals and stone composition. Therefore, the analysis of urine crystals is meaningful for the diagnosis, evaluation of treatment strategies, and prevention of stone recurrence in kidney stone disease. Microscopic analysis of urine crystals allows the observation of smaller crystals. However, manual urine microscopy is slow and time-consuming. To address this, we aim to develop artificial intelligence software to assist in the interpretation of urine crystals, providing a faster analysis. We will retrospectively analyze urine crystal images stored from previous research (Chang Gung Memorial Hospital Internal Project Research No. 107123-E) to identify crystal types. Subsequent image preprocessing and category labeling will be done to train and infer machine software. The results will be compared with manual interpretation to establish the accuracy of the software. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details

NCT number	NCT06178575
Study type	Observational
Source	Far Eastern Memorial Hospital
Contact	Yi-Shiou Tseng
Phone	0920376341
Email	tysgroupone@gmail.com
Status	Not yet recruiting
Phase
Start date	January 1, 2024
Completion date	December 31, 2024

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Clinical trials are conducted in a series of steps, called phases - each phase is designed to answer a separate research question.

Phase 1: Researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety, determine a safe dosage range, and identify side effects.
Phase 2: The drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase 3: The drug or treatment is given to large groups of people to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug or treatment to be used safely.
Phase 4: Studies are done after the drug or treatment has been marketed to gather information on the drug's effect in various populations and any side effects associated with long-term use.

Apply Machine Learning to the Interpretation of Urinary Crystal Morphology.

Clinical Trial Summary

Clinical Trial Description

Study Design

Related Conditions & MeSH terms