Apply Machine Learning to the Interpretation of Urinary Crystal Morphology.

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Kidney Calculi

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 Type: Observational
• Enrollment (Estimated): 200

Contacts and Locations

• Study Contact: Name: Yi-Shiou Tseng; Phone Number: 0920376341; Email: tysgroupone@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Calcium oxalate kidney stone patient

Description

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 Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Kappa statistics	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.	The machine requires approximately 0.5 hours to complete the interpretation of around 800 urine crystal images.

Collaborators and Investigators

• Sponsor: Yi-Shiou Tseng

Study record dates

Study Major Dates

• Study Start (Estimated): January 1, 2024
• Primary Completion (Estimated): December 31, 2024
• Study Completion (Estimated): December 31, 2024

Study Registration Dates

• First Submitted: December 12, 2023
• First Submitted That Met QC Criteria: December 12, 2023
• First Posted (Estimated): December 21, 2023

Study Record Updates

• Last Update Posted (Estimated): December 21, 2023
• Last Update Submitted That Met QC Criteria: December 12, 2023
• Last Verified: December 1, 2023

More Information

Terms related to this study

• Keywords: machine learning; urinary crystal
• Additional Relevant MeSH Terms: Kidney Diseases; Urologic Diseases; Urological Manifestations; Pathological Conditions, Anatomical; Urolithiasis; Urinary Calculi; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Calculi; Kidney Calculi; Nephrolithiasis; Crystalluria
• Other Study ID Numbers: 112183-E

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: This study involves retrospectively analyzing urine crystal images preserved from a previous study (Intramural Research Project Code 107123-E at Far Eastern Memorial Hospital). Subsequently, image preprocessing and category labeling will be applied to facilitate machine software learning and inference. The interpreted results will then undergo statistical analysis for accuracy using dedicated software. Participant information and experimental data are stored on a computer in a shared laboratory, with access secured through password protection to ensure data security. Participant identities are encoded for confidentiality. Once the required information is collected, the original participant identities will be linked with their respective codes. Researchers will not obtain the list of potential participants through privacy-invasive means.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No