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Using artificial intelligence to help detect abnormal blood vessels in the eye

Evaluating a deep learning algorithm in the diagnosis of retinopathy of prematurity (ROP) in Nepal and a prediction model for development of ROP

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ISRCTN
Registry ID
ISRCTN62477762
Enrollment
584
Registered
2025-11-02
Start date
2025-04-01
Completion date
Unknown
Last updated
2025-11-11

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

Conditions

Diagnosis of retinopathy of prematurity Neonatal Diseases

Interventions

Neonates meeting the inclusion criteria were recruited from four study centres (with TUTH and BPKLCOS, both under the Institute of Medicine [IOM], considered as a single centre). Informed consent was

Sponsors

International Centre for Eye Health (ICEH)
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Gestational age 34-36 weeks in children with risk factors such as need of respiratory support, oxygen therapy for more than 6h, sepsis, episodes of apnea and need of blood transfusion, exchange transfusion or unstable clinical course as determined by pediatrician

Exclusion criteria

Exclusion criteria: 1. Any premature babies already treated for ROP 2. Poor image quality of any of images

Design outcomes

Primary

MeasureTime frame
1. Sensitivity of ROP detection is measured using comparison between AI model output and Reference Standard Diagnosis (RSD) from fundus images captured with the Forus camera at each imaging timepoint during two-weekly follow-up until 42 weeks gestational age or complete retinal vascularisation 2. Specificity of ROP detection is measured using comparison between AI model output and Reference Standard Diagnosis (RSD) from fundus images captured with the Forus camera at each imaging timepoint during two-weekly follow-up until 42 weeks gestational age or complete retinal vascularisation

Secondary

MeasureTime frame
There are no secondary outcome measures

Countries

Nepal

Contacts

Public ContactRanjan Shah
ranjan_shah@nnjs.org.np+977 9845325650

Outcome results

None listed

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