Diagnostic Efficacy of CNN in Predicting Intraoperative Complications and Postoperative Outcomes in SMILE

Официальное название

Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Predicting Intraoperative Complications and Postoperative Outcomes in Small Incision Lenticule Extraction

Обзор

To evaluate the diagnostic efficiency of the neural network in predicting complications of Small Incision Lenticule Extraction in a multi-center cross-sectional study.

Подробное описание

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.

Вмешательства

• Диагностический тест AI diagnostic algorithm
  The SMILE procedures collected would be assessed by the algorithm. The performance of the algorithm would be assessed, including accuracy, AUC, sensitivity and specificity.

Первичные конечные точки

• AUROC of convolutional neural network in predicting OBL area [Срок оценки: Day 0]
• AUROC of convolutional neural network in predicting progressive suction loss [Срок оценки: Day 0]
• AUROC of convolutional neural network in predicting effective optical zone [Срок оценки: Day 7]
• AUROC of convolutional neural network in predicting postoperative refractive error [Срок оценки: Day 7]
• AUROC of convolutional neural network in predicting postoperative central corneal thickness [Срок оценки: Day 7]

Вторичные конечные точки (3)

• Sensitivity and specificity of convolutional neural network in predicting OBL area [Срок оценки: Day 0]
• Sensitivity and specificity of convolutional neural network in predicting progressive suction loss [Срок оценки: Day 0]
• Sensitivity and specificity of convolutional neural network in predicting effective optical zone [Срок оценки: Day 7, Day 30, Day 90]

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Другое

Центры проведения

Китай · 1 центр

• The Second Affiliated Hospital of Nanchang University — Nanchang

Идентификаторы

NCT: NCT06204926 · [2023] No.(96)

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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