Dataset Information

Adopting machine learning to automatically identify candidate patients for corneal refractive surgery.

ABSTRACT: Recently, it has become more important to screen candidates that undergo corneal refractive surgery to prevent complications. Until now, there is still no definitive screening method to confront the possibility of a misdiagnosis. We evaluate the possibilities of machine learning as a clinical decision support to determine the suitability to corneal refractive surgery. A machine learning architecture was built with the aim of identifying candidates combining the large multi-instrument data from patients and clinical decisions of highly experienced experts. Five heterogeneous algorithms were used to predict candidates for surgery. Subsequently, an ensemble classifier was developed to improve the performance. Training (10,561 subjects) and internal validation (2640 subjects) were conducted using subjects who had visited between 2016 and 2017. External validation (5279 subjects) was performed using subjects who had visited in 2018. The best model, i.e., the ensemble classifier, had a high prediction performance with the area under the receiver operating characteristic curves of 0.983 (95% CI, 0.977-0.987) and 0.972 (95% CI, 0.967-0.976) when tested in the internal and external validation set, respectively. The machine learning models were statistically superior to classic methods including the percentage of tissue ablated and the Randleman ectatic score. Our model was able to correctly reclassify a patient with postoperative ectasia as an ectasia-risk group. Machine learning algorithms using a wide range of preoperative information achieved a comparable performance to screen candidates for corneal refractive surgery. An automated machine learning analysis of preoperative data can provide a safe and reliable clinical decision for refractive surgery.

SUBMITTER: Yoo TK

PROVIDER: S-EPMC6586803 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Adopting machine learning to automatically identify candidate patients for corneal refractive surgery.

Yoo Tae Keun TK Ryu Ik Hee IH Lee Geunyoung G Kim Youngnam Y Kim Jin Kuk JK Lee In Sik IS Kim Jung Sub JS Rim Tyler Hyungtaek TH

NPJ digital medicine 20190620

Recently, it has become more important to screen candidates that undergo corneal refractive surgery to prevent complications. Until now, there is still no definitive screening method to confront the possibility of a misdiagnosis. We evaluate the possibilities of machine learning as a clinical decision support to determine the suitability to corneal refractive surgery. A machine learning architecture was built with the aim of identifying candidates combining the large multi-instrument data from p ...[more]

PMID: 31304405

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Project description:ImportanceEvaluating corneal morphologic characteristics with corneal tomographic scans before refractive surgery is necessary to exclude patients with at-risk corneas and keratoconus. In previous studies, researchers performed screening with machine learning methods based on specific corneal parameters. To date, a deep learning algorithm has not been used in combination with corneal tomographic scans.ObjectiveTo examine the use of a deep learning model in the screening of candidates for refractive surgery.Design, setting, and participantsA diagnostic, cross-sectional study was conducted at the Zhongshan Ophthalmic Center, Guangzhou, China, with examination dates extending from July 18, 2016, to March 29, 2019. The investigation was performed from July 2, 2018, to June 28, 2019. Participants included 1385 patients; 6465 corneal tomographic images were used to generate the artificial intelligence (AI) model. The Pentacam HR system was used for data collection.InterventionsThe deidentified images were analyzed by ophthalmologists and the AI model.Main outcomes and measuresThe performance of the AI classification system.ResultsA classification system centered on the AI model Pentacam InceptionResNetV2 Screening System (PIRSS) was developed for screening potential candidates for refractive surgery. The model achieved an overall detection accuracy of 94.7% (95% CI, 93.3%-95.8%) on the validation data set. Moreover, on the independent test data set, the PIRSS model achieved an overall detection accuracy of 95% (95% CI, 88.8%-97.8%), which was comparable with that of senior ophthalmologists who are refractive surgeons (92.8%; 95% CI, 91.2%-94.4%) (P = .72). In distinguishing corneas with contraindications for refractive surgery, the PIRSS model performed better than the classifiers (95% vs 81%; P < .001) in the Pentacam HR system on an Asian patient database.Conclusions and relevancePIRSS appears to be useful in classifying images to provide corneal information and preliminarily identify at-risk corneas. PIRSS may provide guidance to refractive surgeons in screening candidates for refractive surgery as well as for generalized clinical application for Asian patients, but its use needs to be confirmed in other populations.

Project description:BackgroundTransepithelial photorefractive keratectomy using Smart Pulse Technology (SPT-TransPRK) is currently the leading method for superficial refractive surgery, offering advantages such as a non-contact procedure, shorter operation times, and excellent patient cooperation. Laser ablation of the corneal epithelium, Bowman's membrane, and the stroma can effectively correct refractive errors. Thus, the complete healing of the corneal epithelium post-surgery is essential for ensuring good vision. Refractive surgeons should enhance their understanding of corneal wound healing mechanisms and focus on the repair of the corneal epithelium following refractive surgery to ensure the quality of visual health of patients.Case presentationA total of five patients experienced varying degrees of delayed corneal epithelial healing following refractive surgery. In Case 1, unhealthy corneal epithelial debris was removed, and ophthalmic ointment was applied to cover the eyes instead of using bandage contact lenses (BCLs) to reconstruct the corneal epithelial barrier. This approach was also successfully implemented in Case 2. Furthermore, amniotic membrane transplantation (AMT) can quickly establish a corneal barrier and promote corneal epithelial regeneration, especially in cases of extensive corneal epithelial detachment. The remaining three patients were suspected of having corneal viral infections based on their medical history and the observation of corneal pathology using a slit lamp microscope. To prevent further infection and promote regeneration, topical steroid drops were discontinued early, and topical antiviral and corneal epithelial regeneration medications were administered alongside systemic antiviral therapy. Steroid drops were resumed after corneal epithelial healing to effectively prevent post-refractive haze.ConclusionDelays in corneal epithelial healing after refractive surgery should be taken seriously. BCLs, steroids, and both topical and systemic antiviral therapies should be properly utilized when there is a delay in corneal epithelial healing.

Project description:BackgroundPythia is an automated, clinically curated surgical data pipeline and repository housing all surgical patient electronic health record (EHR) data from a large, quaternary, multisite health institute for data science initiatives. In an effort to better identify high-risk surgical patients from complex data, a machine learning project trained on Pythia was built to predict postoperative complication risk.Methods and findingsA curated data repository of surgical outcomes was created using automated SQL and R code that extracted and processed patient clinical and surgical data across 37 million clinical encounters from the EHRs. A total of 194 clinical features including patient demographics (e.g., age, sex, race), smoking status, medications, comorbidities, procedure information, and proxies for surgical complexity were constructed and aggregated. A cohort of 66,370 patients that had undergone 99,755 invasive procedural encounters between January 1, 2014, and January 31, 2017, was studied further for the purpose of predicting postoperative complications. The average complication and 30-day postoperative mortality rates of this cohort were 16.0% and 0.51%, respectively. Least absolute shrinkage and selection operator (lasso) penalized logistic regression, random forest models, and extreme gradient boosted decision trees were trained on this surgical cohort with cross-validation on 14 specific postoperative outcome groupings. Resulting models had area under the receiver operator characteristic curve (AUC) values ranging between 0.747 and 0.924, calculated on an out-of-sample test set from the last 5 months of data. Lasso penalized regression was identified as a high-performing model, providing clinically interpretable actionable insights. Highest and lowest performing lasso models predicted postoperative shock and genitourinary outcomes with AUCs of 0.924 (95% CI: 0.901, 0.946) and 0.780 (95% CI: 0.752, 0.810), respectively. A calculator requiring input of 9 data fields was created to produce a risk assessment for the 14 groupings of postoperative outcomes. A high-risk threshold (15% risk of any complication) was determined to identify high-risk surgical patients. The model sensitivity was 76%, with a specificity of 76%. Compared to heuristics that identify high-risk patients developed by clinical experts and the ACS NSQIP calculator, this tool performed superiorly, providing an improved approach for clinicians to estimate postoperative risk for patients. Limitations of this study include the missingness of data that were removed for analysis.ConclusionsExtracting and curating a large, local institution's EHR data for machine learning purposes resulted in models with strong predictive performance. These models can be used in clinical settings as decision support tools for identification of high-risk patients as well as patient evaluation and care management. Further work is necessary to evaluate the impact of the Pythia risk calculator within the clinical workflow on postoperative outcomes and to optimize this data flow for future machine learning efforts.

Dataset Information

Adopting machine learning to automatically identify candidate patients for corneal refractive surgery.

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Adopting machine learning to automatically identify candidate patients for corneal refractive surgery.

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