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Apply Machine Learning to the Interpretation of Urinary Crystal Morphology.

NCT ID: NCT06178575

Last Updated: 2023-12-21

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-01-01

Study Completion Date

2024-12-31

Brief Summary

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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

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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.

Conditions

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Kidney Calculi

Keywords

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machine learning urinary crystal

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

RETROSPECTIVE

Study Groups

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Manual microscopic observation

Control Group: Manual analysis of urine crystal images, distinguishing crystal types, recording accuracy, and analyzing the time consumed.

No interventions assigned to this group

Machine interpretation

The urine crystal images undergo analysis for crystal types, followed by image preprocessing and category labeling for machine software learning and inference. Subsequently, the interpreted results will be subjected to statistical analysis software to assess accuracy.

No interventions assigned to this group

Eligibility Criteria

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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
Minimum Eligible Age

20 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Yi-Shiou Tseng

OTHER

Sponsor Role lead

Responsible Party

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Yi-Shiou Tseng

Attending physician

Responsibility Role SPONSOR_INVESTIGATOR

Central Contacts

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Yi-Shiou Tseng

Role: CONTACT

Phone: 0920376341

Email: [email protected]

Other Identifiers

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112183-E

Identifier Type: -

Identifier Source: org_study_id