Deep Convolutional Neural NetworkNCT06204926
Diagnostic Efficacy of CNN in Predicting Intraoperative Complications and Postoperative Outcomes in SMILE
An observational study of Deep Convolutional Neural Network and Small-incision Lenticule Extraction (SMILE) Surgery, run by Second Affiliated Hospital of Nanchang University at 1 site in China.
Trial summary
To evaluate the diagnostic efficiency of the neural network in predicting complications of Small Incision Lenticule Extraction in a multi-center cross-sectional study. Read more about this study
- Who is it for?
- Ages 18 to 45 yearsHealthy volunteers can take part
- Conditions the study is for
- Deep Convolutional Neural Network, Small-incision Lenticule Extraction (SMILE) Surgery, Intraoperative Complications
- What is being tested?
- AI diagnostic algorithm
- How many people
- 1,250 participants
- Where
- 1 siteChina
- Study dates
- 15 Jun 2021to Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 18 to 45 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: Patients from clinics in different eye centers across China. Each subject must with complete surgical video recording and medical records.
Who may be able to join
- A condition in which the spherical equivalent refractive error of an eye is ≤-0.50 D when ocular accommodation is relaxed;
- Age ≥18 years;
- Spherical equivalent (SE) ≥-10.0D;
- Corrected distance visual acuity (CDVA) ≥16/20;
- Stable myopia for at least 2 years;
- No contact lenses wearing for at least 2 weeks.
Who cannot join
- The presence or history of eye conditions other than myopia and astigmatism, such as keratoconus or external eye injury;
- A history of eye surgery;
- The presence or history of systemic diseases.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The Second Affiliated Hospital of Nanchang University
Nanchang, Jiangxi, 330000
Jian Xiong, doctor, 18170906556 ext. +86, 894040417@qq.com
Fu Gui, doctor, 13879101919 ext. +86, 564436578@qq.com
About this study
Official title: Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Predicting Intraoperative Complications and Postoperative Outcomes in Small Incision Lenticule Extraction
To evaluate the diagnostic efficiency of the neural network in predicting complications of Small Incision Lenticule Extraction in a multi-center cross-sectional study.
Read the detailed description
The primary cause of global visual impairment currently is refractive error, and Small Incision Lenticule Extraction (SMILE) using femtosecond laser for corneal stromal lenticule extraction can alter the refractive power. However, complications such as opaque bubble layer (OBL), negative pressure detachment, and black spots may arise during the SMILE laser scanning process due to individual differences in corneal characteristics, significantly affecting the normal course of surgery and postoperative recovery. Experienced docters can often predict intraoperative complications based on scan images, patient cooperation, and other factors, but the learning curve is relatively long. At present, artificial intelligence has achieved the accuracy comparable to human physicians in the interpretation of medical imaging of many different diseases.Previously, we have trained a deep convolutional neural network for predicting intraoperative complications in SMILE procedures. The current multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in predicting intraoperative complications and to assess its utility in the real world.
- Conditions
- Deep Convolutional Neural Network, Small-incision Lenticule Extraction (SMILE) Surgery, Intraoperative Complications, Postoperative Outcomes
- Being tested
- AI diagnostic algorithm
- Keywords
- femtosecond laser small-incision lenticule extraction, Intraoperative Complications, Deep Convolutional Neural Network, Postoperative Outcomes
- Medical subject headings
- Smiling, Intraoperative Complications
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI diagnostic algorithmDiagnostic test
The SMILE procedures collected would be assessed by the algorithm. The performance of the algorithm would be assessed, including accuracy, AUC, sensitivity and specificity.
| Study group | Role | What they receive |
|---|---|---|
| Eyes with SMILE surgeries | Eyes with SMILE surgeries which were performed by surgeons with experiences. Receives: Diagnostic Test: AI diagnostic algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
AUROC of convolutional neural network in predicting OBL area
The area under the receiver operating characteristic of convolutional neural network in predicting opaque bubble layer area during the SMILE surgeries
Measured: Day 0
AUROC of convolutional neural network in predicting progressive suction loss
The area under the receiver operating characteristic of convolutional neural network in predicting progressive suction loss during the SMILE surgeries
Measured: Day 0
AUROC of convolutional neural network in predicting effective optical zone
The area under the receiver operating characteristic of convolutional neural network in predicting effective optical zone after the SMILE surgeries
Measured: Day 7
AUROC of convolutional neural network in predicting postoperative refractive error
The area under the receiver operating characteristic of convolutional neural network in predicting refractive error after the SMILE surgeries
Measured: Day 7
AUROC of convolutional neural network in predicting postoperative central corneal thickness
The area under the receiver operating characteristic of convolutional neural network in predicting central corneal thickness after the SMILE surgeries
Measured: Day 7
Secondary outcomes (3)
Sensitivity and specificity of convolutional neural network in predicting OBL area
Sensitivity and specificity of convolutional neural network in predicting opaque bubble layer area during the SMILE surgeries
Measured: Day 0
Sensitivity and specificity of convolutional neural network in predicting progressive suction loss
Sensitivity and specificity of convolutional neural network in predicting progressive suction loss during the SMILE surgeries
Measured: Day 0
Sensitivity and specificity of convolutional neural network in predicting effective optical zone
ensitivity and specificity of convolutional neural network in predicting effective optical zone after the SMILE surgeries
Measured: Day 7, Day 30, Day 90
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jan 2024
- Study start15 Jun 2021
- Primary completionDec 2027 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Deep Convolutional Neural NetworkStudies of the same condition, open and under way.
- More trials run by Second Affiliated Hospital of Nanchang UniversityThe sponsor's other registered studies.
- 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
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 1,250 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Hangzhou Huaxia Eye Hospital, Nanchang Bright Eye Hospital
- Responsible party
- Principal investigator, Jian Xiong, Second Affiliated Hospital of Nanchang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06204926
- Sponsor's protocol ID
- [2023] No.(96)
- Status last verified
- Jan 2026
- First submitted
- 3 Jan 2024
- Last updated
- 24 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Predicting Intraoperative Complications and Postoperative Outcomes in Small Incision Lenticule Extraction
- ClinicalTrials.gov ID
- NCT06204926
- Acronym / sponsor ID
- [2023] No.(96)
- Conditions
- Deep Convolutional Neural Network, Small-incision Lenticule Extraction (SMILE) Surgery, Intraoperative Complications, Postoperative Outcomes
- Keywords
- femtosecond laser small-incision lenticule extraction, Intraoperative Complications, Deep Convolutional Neural Network, Postoperative Outcomes
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,250 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital of Nanchang UniversityUniversity, hospital or other
- Collaborators
- Hangzhou Huaxia Eye Hospital, Nanchang Bright Eye Hospital
- Study start
- 15 Jun 2021 (actual)
- Primary completion
- Dec 2027 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 12 Jan 2024 (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.
To evaluate the diagnostic efficiency of the neural network in predicting complications of Small Incision Lenticule Extraction in a multi-center cross-sectional study.
Detailed description
The primary cause of global visual impairment currently is refractive error, and Small Incision Lenticule Extraction (SMILE) using femtosecond laser for corneal stromal lenticule extraction can alter the refractive power. However, complications such as opaque bubble layer (OBL), negative pressure detachment, and black spots may arise during the SMILE laser scanning process due to individual differences in corneal characteristics, significantly affecting the normal course of surgery and postoperative recovery. Experienced docters can often predict intraoperative complications based on scan images, patient cooperation, and other factors, but the learning curve is relatively long. At present, artificial intelligence has achieved the accuracy comparable to human physicians in the interpretation of medical imaging of many different diseases.Previously, we have trained a deep convolutional neural network for predicting intraoperative complications in SMILE procedures. The current multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in predicting intraoperative complications and to assess its utility in the real world.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Eyes with SMILE surgeries | – | Diagnostic Test: AI diagnostic algorithmEyes with SMILE surgeries which were performed by surgeons with experiences. |
Who may qualify
- Ages
- 18 to 45 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- Patients from clinics in different eye centers across China. Each subject must with complete surgical video recording and medical records.
Key inclusion criteria (6 listed)
- A condition in which the spherical equivalent refractive error of an eye is ≤-0.50 D when ocular accommodation is relaxed;
- Age ≥18 years;
- Spherical equivalent (SE) ≥-10.0D;
- Corrected distance visual acuity (CDVA) ≥16/20;
- Stable myopia for at least 2 years;
Key exclusion criteria (3 listed)
- The presence or history of eye conditions other than myopia and astigmatism, such as keratoconus or external eye injury;
- A history of eye surgery;
- The presence or history of systemic diseases.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI diagnostic algorithmThe SMILE procedures collected would be assessed by the algorithm. The performance of the algorithm would be assessed, including accuracy, AUC, sensitivity and specificity. | Diagnostic test | Eyes with SMILE surgeries |
Primary outcome
AUROC of convolutional neural network in predicting OBL area
Time frame: Day 0
The area under the receiver operating characteristic of convolutional neural network in predicting opaque bubble layer area during the SMILE surgeries
AUROC of convolutional neural network in predicting progressive suction loss
Time frame: Day 0
The area under the receiver operating characteristic of convolutional neural network in predicting progressive suction loss during the SMILE surgeries
AUROC of convolutional neural network in predicting effective optical zone
Time frame: Day 7
The area under the receiver operating characteristic of convolutional neural network in predicting effective optical zone after the SMILE surgeries
AUROC of convolutional neural network in predicting postoperative refractive error
Time frame: Day 7
The area under the receiver operating characteristic of convolutional neural network in predicting refractive error after the SMILE surgeries
AUROC of convolutional neural network in predicting postoperative central corneal thickness
Time frame: Day 7
The area under the receiver operating characteristic of convolutional neural network in predicting central corneal thickness after the SMILE surgeries
3 secondary outcomes
Sensitivity and specificity of convolutional neural network in predicting OBL area
Time frame: Day 0
Sensitivity and specificity of convolutional neural network in predicting progressive suction loss
Time frame: Day 0
Sensitivity and specificity of convolutional neural network in predicting effective optical zone
Time frame: Day 7, Day 30, Day 90
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Apr 2026.
- Individual participant dataSharing planned: No
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Deep Convolutional Neural Network and Small-incision Lenticule Extraction (SMILE) Surgery, run by Second Affiliated Hospital of Nanchang University at 1 site in China.
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 6 inclusion and 3 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: looking for participants now, as of the sponsor's last update on 24 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jian Xiong, docter (18170906556 ext. +86, 894040417@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUROC of convolutional neural network in predicting OBL area (time frame: Day 0). The study lists 5 primary outcomes in all. 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 24 Apr 2026.