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Optimizing Nomogram in SMILE Surgery Based on Machine Learning Technology: a Multicenter Study

Optimizing Nomogram in SMILE Surgery Based on Machine Learning Technology: a Multicenter Study

Status
Recruiting
Phases
Unknown
Study type
Interventional
Source
ChiCTR
Registry ID
ChiCTR1900025740
Enrollment
Unknown
Registered
2019-09-07
Start date
2019-09-02
Completion date
Unknown
Last updated
2019-09-09

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

Conditions

Refractive surgery

Interventions

Group1:machine learning technique for prediction of small incision lenticule extraction(SMILE) nomogram
Group2:ophthalmologist operation

Sponsors

Tianjin Eye Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. aged 18 to 45 years; 2. spherical myopia up to -9.00D, and myopic astigmatism up to -4.00D; 3. corrected distance visual acuity (CDVA) of 20/40 or better; 4. postoperative residual stromal bed thickness > 250um; 5. stable refraction for more than 2 years.

Exclusion criteria

Exclusion criteria: 1) corneal disease; 2) ocular trauma; 3) suspicion of keratoconus on corneal topography. Patients were required to stop wearing soft contact lenses for at least 2 weeks and rigid contact lenses for at least 4 weeks before examination; 4)other systemic diseases.

Design outcomes

Primary

MeasureTime frame
BCVA preopertive;preperation refraction;UDVA preoperative;BCVA postoperation;refraction postoperation;UDVA postoperation;—

Countries

China

Contacts

Public ContactWang Yan

Tianjin Eye Hospital

wangyan7143@vip.sina.com+86 13602089393

Outcome results

None listed

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