Role of artificial intelligence in determining factors impacting patients' refractive surgery decisions

Purpose: To create a predictive model using artificial intelligence (AI) and assess if available data from patients' registration records can help in predicting definitive endpoints such as the probability of patients signing up for refractive surgery. Methods: This was a retrospective analysis. Electronic health records data of 423 patients presenting to the refractive surgery department were incorporated into models using multivariable logistic regression, decision trees classifier, and random forest (RF). Mean area under the receiver operating characteristic curve (ROC-AUC), sensitivity (Se), specificity (Sp), classification accuracy, precision, recall, and F1-score were calculated for each model to evaluate performance. Results: The RF classifier provided the best output among the various models, and the top variables identified in this study by the RF classifier excluding income were insurance, time spent in the clinic, age, occupation, residence, source of referral, and so on. About 93% of the cases that did undergo refractive surgery were correctly predicted as having undergone refractive surgery. The AI model achieved an ROC-AUC of 0.945 with an Se of 88% and Sp of 92.5%. Conclusion: This study demonstrated the importance of stratification and identifying various factors using an AI model which could impact patients' decisions while selecting a refractive surgery. Eye centers can build specialized prediction profiles across disease categories and may allow for the identification of prospective obstacles in the patient's decision-making process, as well as strategies for dealing with them

Coronavirus disease 19 (COVID-19) and viral keratouveitis – unraveling the mystery

To demonstrate viral proteins/inflammatory cytokines in a patient with unilateral keratouveitis. Retrospective case report. A 70-year-old Asian-Indian male presented with acute onset of blurring of vision in the left eye (OS) of 2 days duration. He had was coronavirus disease 2019 (COVID-19)-positive 3 months earlier. He had undergone cataract surgery/retinal laser photocoagulation in both the eyes. The corrected distance visual acuity (CDVA) (Snellen) in the right eye (RE) (OD) and left eye (LE) (OS) was 20/20 and 20/80, respectively. OS showed decreased corneal sensation, Descemet's folds, mild stromal edema, and fine and pigmented keratic precipitates with anterior chamber 1+ flare and 1+ cells. Fundus evaluation showed scattered laser marks in the OD and temporal sectoral laser marks in OS. He was diagnosed with viral keratouveitis in OS. Tear samples were collected on Schirmer's strips and tear wash for mass spectrometry and cytokines, which had 368 and 451 viral proteins in the RE and LE, respectively, using nano liquid chromatography–mass spectrometry, which were more than controls. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and varicella zoster virus proteins were detected. Cytokine analysis using flow cytometer analysis showed higher inflammation in OS as compared to OD. The patient was treated with oral acyclovir and topical steroids and resulted in resolution of his keratouveitis. SARS-CoV-2 proteins were present in the tear sample 3 months after COVID-19. The presence of viral proteins does not indicate causality

A novel combination of corneal confocal microscopy, clinical features and artificial intelligence for evaluation of ocular surface pain

OBJECTIVES: To analyse various corneal nerve parameters using confocal microscopy along with systemic and orthoptic parameters in patients presenting with ocular surface pain using a random forest artificial intelligence (AI) model. DESIGN: Observational, cross-sectional. METHODS: Two hundred forty eyes of 120 patients with primary symptom of ocular surface pain or discomfort and control group of 60 eyes of 31 patients with no symptoms of ocular pain were analysed. A detailed ocular examination included visual acuity, refraction, slit-lamp and fundus. All eyes underwent laser scanning confocal microscopy (Heidelberg Engineering, Germany) and their nerve parameters were evaluated. The presence or absence of orthoptic issues and connective tissue disorders were included in the AI. The eyes were grouped as those (Group 1) with symptom grade higher than signs, (Group 2) with similar grades of symptoms and signs, (Group3) without symptoms but with signs, (Group 4) without symptoms and signs. The area under curve (AUC), accuracy, recall, precision and F1-score were evaluated. RESULTS: Over all, the AI achieved an AUC of 0.736, accuracy of 86%, F1-score of 85.9%, precision of 85.6% and recall of 86.3%. The accuracy was the highest for Group 2 and least for Group 3 eyes. The top 6 parameters used for classification by the AI were microneuromas, immature and mature dendritic cells, presence of orthoptic issues and nerve fractal dimension parameter. CONCLUSIONS: This study demonstrated that various corneal nerve parameters, presence or absence of systemic and orthoptic issues coupled with AI can be a useful technique to understand and correlate the various clinical and imaging parameters of ocular surface pain

Epithelium Zernike Indices and Artificial Intelligence Can Differentiate Epithelial Remodeling Between Flap and Flapless Refractive Procedures

PURPOSE: To evaluate epithelial Zernike indices as a differentiator of epithelial remodeling after different refractive procedures. METHODS: Optical coherence tomography [OCT] images of 22 laser in situ keratomileusis, 22 small incision lenticule extraction, 15 photorefractive keratectomy [PRK], and 17 transepithelial PRK eyes were evaluated retrospectively before and after surgery. A custom algorithm was used to calculate the epithelial Zernike indices from the three-dimensional distribution of epithelial thickness distribution. The epithelial Zernike indices were also compared with the local measurements of epithelial thickness, used conventionally from the current clinical OCT. A decision tree classifier was built, one in which flap/ cap and surface procedures were classified [2G] and another in which all surgical groups were classified separately [4G]. RESULTS: Local measurements of thicknesses changed significantly after all surgeries (P .05). The surgeries not only changed the epithelial Zernike indices (P < .05), but also resulted in differential changes in epithelial thickness distribution based on the type of surgery (P < .05). In the 2G analyses with local measurements of epithelial thickness, the area under the curve, sensitivity, and specificity were 0.57 +/- 0.07, 42.11%, and 57.89%, respectively. Further, the accuracy was limited to less than 60%. In the 2G analyses with epithelial Zernike indices, the area under the curve, sensitivity, and specificity were 0.79 +/- 0.05, 86.4%, and 71.9%, respectively. Here, the accuracy was limited between 70% and 80%. Similar trends were observed with 4G analyses. CONCLUSIONS: The epithelial Zernike indices were significantly better in identifying surgery-specific three-dimensional remodeling of the thickness compared to local measurements of epithelial thickness. Further, the changes in Zernike indices were independent of the magnitude of refractive error but not the type of surgery

Epithelium Zernike Indices and Artificial Intelligence Can Differentiate Epithelial Remodeling Between Flap and Flapless Refractive Procedures

PURPOSE: To evaluate epithelial Zernike indices as a differentiator of epithelial remodeling after different refractive procedures. METHODS: Optical coherence tomography [OCT] images of 22 laser in situ keratomileusis, 22 small incision lenticule extraction, 15 photorefractive keratectomy [PRK], and 17 transepithelial PRK eyes were evaluated retrospectively before and after surgery. A custom algorithm was used to calculate the epithelial Zernike indices from the three-dimensional distribution of epithelial thickness distribution. The epithelial Zernike indices were also compared with the local measurements of epithelial thickness, used conventionally from the current clinical OCT. A decision tree classifier was built, one in which flap/ cap and surface procedures were classified [2G] and another in which all surgical groups were classified separately [4G]. RESULTS: Local measurements of thicknesses changed significantly after all surgeries (P .05). The surgeries not only changed the epithelial Zernike indices (P < .05), but also resulted in differential changes in epithelial thickness distribution based on the type of surgery (P < .05). In the 2G analyses with local measurements of epithelial thickness, the area under the curve, sensitivity, and specificity were 0.57 +/- 0.07, 42.11%, and 57.89%, respectively. Further, the accuracy was limited to less than 60%. In the 2G analyses with epithelial Zernike indices, the area under the curve, sensitivity, and specificity were 0.79 +/- 0.05, 86.4%, and 71.9%, respectively. Here, the accuracy was limited between 70% and 80%. Similar trends were observed with 4G analyses. CONCLUSIONS: The epithelial Zernike indices were significantly better in identifying surgery-specific three-dimensional remodeling of the thickness compared to local measurements of epithelial thickness. Further, the changes in Zernike indices were independent of the magnitude of refractive error but not the type of surgery

Clinical and Molecular Outcomes After Combined Intense Pulsed Light Therapy With Low-Level Light Therapy in Recalcitrant Evaporative Dry Eye Disease With Meibomian Gland Dysfunction

PURPOSE: Dry eye disease (DED) is a leading cause of ocular morbidity worldwide. This study evaluates the effects of combined light therapy [intense pulsed light (IPL) and low-level light therapy (LLLT)] on clinical and molecular outcomes in evaporative DED with meibomian gland dysfunction (MGD). METHODS: This prospective study evaluated 94 eyes (47 subjects) with chronic MGD treated with combined light therapy. Patients underwent a detailed evaluation of MGD and DED using the Ocular Surface Disease Index, dry eye tests-tear breakup time and Schirmer test, ocular surface staining, meibomian gland expressibility scoring, and meibography. Patients underwent a single session of combined light therapy (IPL + LLLT treatment) using the Eye-light device. All these tests were repeated at 3 and 6 months after treatment. Tear fluid and ocular surface wash samples were collected from a subset of patients before and after treatment for cellular and secreted immune factor profiling by flow cytometry. RESULTS: Combined light therapy (IPL + LLLT) demonstrated a marked improvement in the clinical metrics studied. Three months after treatment, Ocular Surface Disease Index showed a significant reduction in 95.6% (P < 0.0001), tear breakup time increased in 72.3% (P < 0.0001), and meibomian gland expressibility scoring increased in 80.8% (P < 0.0001) of the eyes. These effects were observed to be sustained during the 6-month follow-up visit. Significant (P < 0.05) reduction in tear fluid levels of interleukin-1β, interleukin-17F, and MMP9; MMP9/TIMP1 ratio; and ocular surface B-cell proportions was observed. CONCLUSIONS: Combined light therapy shows promising results in patients with chronic MGD and DED, even in recalcitrant cases. Clinical and molecular factor alterations support the improved symptomatology and reduced inflammation

Artificial intelligence–based stratification of demographic, ocular surface high-risk factors in progression of keratoconus

Purpose: The purpose of this study was to identify and analyze the clinical and ocular surface risk factors influencing the progression of keratoconus (KC) using an artificial intelligence (AI) model. Methods: This was a prospective analysis in which 450 KC patients were included. We used the random forest (RF) classifier model from our previous study (which evaluated longitudinal changes in tomographic parameters to predict “progression” and “no progression”) to classify these patients. Clinical and ocular surface risk factors were determined through a questionnaire, which included presence of eye rubbing, duration of indoor activity, usage of lubricants and immunomodulator topical medications, duration of computer use, hormonal disturbances, use of hand sanitizers, immunoglobulin E (IgE), and vitamins D and B12 from blood investigations. An AI model was then built to assess whether these risk factors were linked to the future progression versus no progression of KC. The area under the curve (AUC) and other metrics were evaluated. Results: The tomographic AI model classified 322 eyes as progression and 128 eyes as no progression. Also, 76% of the cases that were classified as progression (from tomographic changes) were correctly predicted as progression and 67% of cases that were classified as no progression were predicted as no progression based on clinical risk factors at the first visit. IgE had the highest information gain, followed by presence of systemic allergies, vitamin D, and eye rubbing. The clinical risk factors AI model achieved an AUC of 0.812. Conclusion: This study demonstrated the importance of using AI for risk stratification and profiling of patients based on clinical risk factors, which could impact the progression in KC eyes and help manage them better

Status of Residual Refractive Error, Ocular Aberrations, and Accommodation After Myopic LASIK, SMILE, and TransPRK

PURPOSE: To analyze residual refractive error, ocular aberrations, and visual acuity (VA) during accommodation simultaneously with ocular aberrometry in eyes after laser-assisted in situ keratomileusis (LASIK), small incision lenticule extraction (SMILE), and transepithelial photorefractive keratectomy (TransPRK). METHODS: Ocular aberrometry (Tracey Technologies, Houston, TX) was performed 3 months after LASIK (n = 95), SMILE (n = 73), and TransPRK (n = 35). White measuring the aberrations, VA was measured at distance (20 ft), intermediate (60 cm), and near (40 cm) targets. The examinations were done monocularly. A parallel group of age-matched normal eyes (n = 50) with 20/20 Snellen distance VA also underwent aberrometry. RESULTS: Distribution of residual spherical error of LASIK eyes matched the normal eyes the best, followed by SMILE and TransPRK. However, the distribution of cylindrical error of the SMILE eyes was distinctly different from the rest (P <.05). The SMILE eyes tended to be undercorrected by approximately 0.25 diopters (D) on average at all reading targets compared to LASIK eyes (P <.05). The undercorrection was greater when the magnitude of the preoperative cylinder exceeded 0.75 D (P <.05). The VA of LASIK and SMILE eyes was similar to normal eyes at all targets, but the TransPRK eyes were marginally inferior (P <.05). Only the ocular defocus changed differentially between the study groups during accommodation and the magnitude of change was least for TransPRK eyes (P <.05). However, postoperative near and intermediate accommodation of LASIK eyes were similar to normal eyes, followed by SMILE eyes and then TransPRK eyes. CONCLUSIONS: The refractive and aberrometric status of the LASIK eyes was closest to the normal eyes. The SMILE procedure may benefit from slight overcorrection of the preoperative refractive cylinder

Status of Residual Refractive Error, Ocular Aberrations, and Accommodation After Myopic LASIK, SMILE, and TransPRK

PURPOSE: To analyze residual refractive error, ocular aberrations, and visual acuity (VA) during accommodation simultaneously with ocular aberrometry in eyes after laser-assisted in situ keratomileusis (LASIK), small incision lenticule extraction (SMILE), and transepithelial photorefractive keratectomy (TransPRK). METHODS: Ocular aberrometry (Tracey Technologies, Houston, TX) was performed 3 months after LASIK (n = 95), SMILE (n = 73), and TransPRK (n = 35). White measuring the aberrations, VA was measured at distance (20 ft), intermediate (60 cm), and near (40 cm) targets. The examinations were done monocularly. A parallel group of age-matched normal eyes (n = 50) with 20/20 Snellen distance VA also underwent aberrometry. RESULTS: Distribution of residual spherical error of LASIK eyes matched the normal eyes the best, followed by SMILE and TransPRK. However, the distribution of cylindrical error of the SMILE eyes was distinctly different from the rest (P <.05). The SMILE eyes tended to be undercorrected by approximately 0.25 diopters (D) on average at all reading targets compared to LASIK eyes (P <.05). The undercorrection was greater when the magnitude of the preoperative cylinder exceeded 0.75 D (P <.05). The VA of LASIK and SMILE eyes was similar to normal eyes at all targets, but the TransPRK eyes were marginally inferior (P <.05). Only the ocular defocus changed differentially between the study groups during accommodation and the magnitude of change was least for TransPRK eyes (P <.05). However, postoperative near and intermediate accommodation of LASIK eyes were similar to normal eyes, followed by SMILE eyes and then TransPRK eyes. CONCLUSIONS: The refractive and aberrometric status of the LASIK eyes was closest to the normal eyes. The SMILE procedure may benefit from slight overcorrection of the preoperative refractive cylinder