Kidney CalculiNCT06178575
Apply Machine Learning to the Interpretation of Urinary Crystal Morphology.
An observational study of Kidney Calculi, run by Yi-Shiou Tseng.
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… Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Kidney Calculi
- How many people
- 200 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2024to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Calcium oxalate kidney stone patient
Who may be able to join
- 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.
Who cannot join
- Not applicable
Trial locations and contacts
No sites are listed yet.
About this study
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.
Read the 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.
- Conditions
- Kidney Calculi
- Keywords
- machine learning, urinary crystal
- Medical subject headings
- Kidney Calculi
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: The machine requires approximately 0.5 hours to complete the interpretation of around 800 urine crystal images.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Dec 2023
- Study start1 Jan 2024 (estimated)
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Kidney CalculiStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Yi-Shiou Tseng
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Yi-Shiou Tseng, Far Eastern Memorial Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06178575
- Sponsor's protocol ID
- 112183-E
- Status last verified
- Dec 2023
- First submitted
- 12 Dec 2023
- Last updated
- 21 Dec 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 21 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Apply Machine Learning to the Interpretation of Urinary Crystal Morphology.
- ClinicalTrials.gov ID
- NCT06178575
- Acronym / sponsor ID
- 112183-E
- Conditions
- Kidney Calculi
- Keywords
- machine learning, urinary crystal
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Yi-Shiou TsengUniversity, hospital or other
- Study start
- 1 Jan 2024 (estimated)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 21 Dec 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Calcium oxalate kidney stone patient
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (1 listed)
- Not applicable
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Kappa statistics
Time frame: 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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Dec 2023.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Questions about this trial
What is this clinical trial studying?
An observational study of Kidney Calculi, run by Yi-Shiou Tseng.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 21 Dec 2023. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yi-Shiou Tseng (0920376341, tysgroupone@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Kappa statistics (time frame: The machine requires approximately 0.5 hours to complete the interpretation of around 800 urine crystal images.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Dec 2023.