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Use of phone based application to track patients with stents( tubes placed between kidney and urinary bladder)after surgery for stones in kidney , so asto prevent retained stents and related problems.

Artificial Intelligence Based Identification of kidney stone and Prediction of Kidney Stone Composition and Prevention of its Recurrence- a Single Centre Prospective Observational Study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2020/09/027665
Enrollment
500
Registered
2020-09-08
Start date
Unknown
Completion date
Unknown
Last updated
2021-11-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: N00-N99- Diseases of the genitourinary system

Interventions

None listed

Sponsors

Kasturba Medical College
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Any patient having kidney and ureteric stone and undergoing endoscopic procedure.

Exclusion criteria

Exclusion criteria: Patient not willing to participate in the study, Pregnancy (cannot undergo NCCT)

Design outcomes

Primary

MeasureTime frame
Accuracy of deep learning and artificial intelligence techniques in identifying the renal stones. - Accuracy of Deep learning and Artificial Intelligence techniques in identifying the stone composition as compared to stone analysis Timepoint: data is collected after the patinet undergoes CT scan—

Secondary

MeasureTime frame
The decrease in recurrence rate of stones in patients after starting treatment based on stone composition detected through deep learning and artificial intelligence.Timepoint: After the initial treatment, during follow up—

Countries

India

Contacts

Public ContactMilap Shah

Kasturba Medical College, Manipal

drmilapshah@gmail.com8141200532

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026