Apply Machine Learning to the Interpretation of Urinary Crystal Morphology.

Not yet recruiting

• Conditions: Kidney Calculi

• Registration Number: NCT06178575
• Lead Sponsor: Yi-Shiou Tseng

Brief 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.

Detailed 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 Timeline

• Status Verified: 2023-12
• Study First Submitted: 2023-12-12
• Study First Submitted QC: 2023-12-12
• Last Update Submitted: 2023-12-12
• Study First Posted: 2023-12-21
• Last Update Posted: 2023-12-21
• Study Start: 2024-01-01
• Primary Completion: 2024-12-31
• Study Completion: 2024-12-31

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 200

Inclusion Criteria

• Retrospectively analyze the urine crystal images preserved from the previous study 107123-E for crystal type analysis. Subsequently, conduct image preprocessing and label categorization for machine software learning and inference. The interpreted results will then be assessed for accuracy using statistical analysis software.

Exclusion Criteria

• Not applicable

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Kappa statistics	The machine requires approximately 0.5 hours to complete the interpretation of around 800 urine crystal images.	Used for comparing between a new instrument and a standard instrument to determine whether the new instrument exhibits a certain level of performance or accuracy.