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10 pages, 2584 KiB

Open AccessArticle

Transepithelial Enhanced Fluence Pulsed Light M Accelerated Crosslinking for Early Progressive Keratoconus with Chemically Enhanced Riboflavin Solutions and Air Room Oxygen

by Cosimo Mazzotta, Ashraf Armia Balamoun, Ayoub Chabib, Miguel Rechichi, Francesco D’Oria, Farhad Hafezi, Simone Alex Bagaglia and Marco Ferrise

J. Clin. Med. 2022, 11(17), 5039; https://doi.org/10.3390/jcm11175039 - 27 Aug 2022

Cited by 5 | Viewed by 1360

Abstract

Purpose: To assess the 3-year clinical results of the 18 mW 7 J/cm² transepithelial enhanced fluence pulsed light M accelerated crosslinking in the treatment of progressive keratoconus (KC) with chemically enhanced hyper-concentrated riboflavin solutions without iontophoresis and with air-room oxygenation. Setting: Siena [...] Read more.

Purpose: To assess the 3-year clinical results of the 18 mW 7 J/cm² transepithelial enhanced fluence pulsed light M accelerated crosslinking in the treatment of progressive keratoconus (KC) with chemically enhanced hyper-concentrated riboflavin solutions without iontophoresis and with air-room oxygenation. Setting: Siena Crosslinking Center, Siena, Italy. Methods: Prospective pilot, open non-randomized interventional study including 40 eyes of 30 young adult patients over 21 years old (10 simultaneous bilateral) with early (Stage I and II) progressive KC undergoing TE-EFPL 18 mW/7 J/cm² ACXL (EFPL M TECXL). The 12 min and 58 s pulsed light (1 s on/1 s off) UV-A exposure treatments were performed with a biphasic corneal soaking using Paracel I 0.25% for 4 min and Paracel II 0.22% for 6 min riboflavin solutions and New KXL I UV-A emitter (Glaukos-Avedro, Waltham, USA) at an air room of 21% oxygenation. All patients completed the 3-year follow-up. Results: CDVA showed a statistically significant improvement in the third postoperative month (Δ + 0.17 d. e.) with a final gain of +0.22 d. eq. AK showed a statistically significant decrease in the sixth postoperative month (Δ − 1.15 diopters). K itmax showed a statistically significant decrease at 1-year follow-up (Δ − 1.3 diopters). The coma value improved significantly by the sixth month (Δ − 0.54 µm). MCT remained stable during the entire follow-up. No adverse events were recorded. Corneal OCT revealed a mean demarcation line depth at 282.6 ± 23.6 μm. Conclusions: Transepithelial enhanced fluence pulsed light M accelerated crosslinking with chemically enhanced riboflavin solution halted KC progression in young adult patients without iontophoresis and no intraoperative oxygen supplementation addressing the importance of increased fluence. Full article

(This article belongs to the Special Issue Innovations in Keratoconus Diagnosis and Management)

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10 pages, 3952 KiB

Open AccessArticle

Retrospective Analysis of Sterile Corneal Infiltrates in Patients with Keratoconus after Cross-Linking Procedure

by Magdalena Krok, Ewa Wróblewska-Czajka, Joanna Kokot, Anna Micińska, Edward Wylęgała and Dariusz Dobrowolski

J. Clin. Med. 2022, 11(3), 585; https://doi.org/10.3390/jcm11030585 - 25 Jan 2022

Cited by 2 | Viewed by 2287

Abstract

Background: This paper’s objective is to analyze patients with keratoconus who developed sterile infiltrate after corneal collagen cross-linking (CXL), and to evaluate possible risk factors for their occurrence. Methods: 543 medical histories of patients after cross-linking (Epi-off, Epi-on) procedure performed according to the [...] Read more.

Background: This paper’s objective is to analyze patients with keratoconus who developed sterile infiltrate after corneal collagen cross-linking (CXL), and to evaluate possible risk factors for their occurrence. Methods: 543 medical histories of patients after cross-linking (Epi-off, Epi-on) procedure performed according to the Dresden protocol were analyzed retrospectively. Results: Sterile corneal infiltrates occurred in four men (0.7%) in the age range (16–28) years, the average age being 20.3. The average time from procedure to onset of symptoms was 3.5 days (2–5 days). Inflammatory infiltration resolved in all patients, leaving scars on corneal stroma in two patients. Corneal healing time ranged from 4–12 weeks. In vivo confocal microscopy (IVCM), round inflammatory cells, and Langerhans cells in the epithelium and Bowman’s layer were observed at the site of infiltration. The Optical coherence tomography (OCT) shows hyperreflective lesions of various sizes which decreased over time. The corneal topographic parameters and Best-corrected visual acuity (BCVA) improved after the CXL procedure in all of the described cases. Conclusions: Most likely, damage to the epithelium and the phototoxic effect of the procedure is of significant importance in the formation of sterile corneal infiltrates. Appropriate classification and selection of CXL procedures in combination with protective measures in people at risk may have an overwhelming impact on the incidence of this complication. Full article

(This article belongs to the Special Issue Innovations in Keratoconus Diagnosis and Management)

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12 pages, 4116 KiB

Open AccessArticle

Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques

by Gracia Castro-Luna, Diana Jiménez-Rodríguez, Ana Belén Castaño-Fernández and Antonio Pérez-Rueda

J. Clin. Med. 2021, 10(18), 4281; https://doi.org/10.3390/jcm10184281 - 21 Sep 2021

Cited by 12 | Viewed by 2397

Abstract

(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a [...] Read more.

(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) < 46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0°, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time. Full article

(This article belongs to the Special Issue Innovations in Keratoconus Diagnosis and Management)

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11 pages, 925 KiB

Open AccessArticle

Diagnosis of Subclinical Keratoconus with a Combined Model of Biomechanical and Topographic Parameters

by Antonio Pérez-Rueda, Diana Jiménez-Rodríguez and Gracia Castro-Luna

J. Clin. Med. 2021, 10(13), 2746; https://doi.org/10.3390/jcm10132746 - 22 Jun 2021

Cited by 9 | Viewed by 2072

Abstract

This study sought to develop a diagnostic model with aberrometry and biomechanical variables for subclinical keratoconus. The design was a cross-sectional study. The topographic data were obtained with a rotating Scheimpflug camera (Pentacam HR), and biomechanical data were obtained with Corvis ST. The [...] Read more.

This study sought to develop a diagnostic model with aberrometry and biomechanical variables for subclinical keratoconus. The design was a cross-sectional study. The topographic data were obtained with a rotating Scheimpflug camera (Pentacam HR), and biomechanical data were obtained with Corvis ST. The study included 81 eyes distributed in 61 healthy corneas and 20 subclinical keratoconus (SCKC), defined as eyes with suspicious topographic findings, normal slit-lamp examination, and a manifestation of keratoconus. Analyses of the topographic and biomechanical data were performed, and a classifying model of SCKC was elaborated. The model for the diagnosis of SCKC includes posterior coma to 90°, Ambrósio’s Relational Thickness in the horizontal profile (ARTh), and velocity when the air pulse is off (A2 velocity). The sensitivity was 89.5%, specificity 96.7%, accuracy 94.9%, and precision 89.5%. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the model was 0.951. Diagnosis of subclinical keratoconus depends on the aberrometry variable posterior coma to 90° and the biomechanical variables A2 velocity and ARTh. Full article

(This article belongs to the Special Issue Innovations in Keratoconus Diagnosis and Management)

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14 pages, 2666 KiB

Open AccessArticle

Chemically-Boosted Corneal Cross-Linking for the Treatment of Keratoconus through a Riboflavin 0.25% Optimized Solution with High Superoxide Anion Release

by Cosimo Mazzotta, Marco Ferrise, Guido Gabriele, Paolo Gennaro and Alessandro Meduri

J. Clin. Med. 2021, 10(6), 1324; https://doi.org/10.3390/jcm10061324 - 23 Mar 2021

Cited by 10 | Viewed by 2605

Abstract

The purpose of this study was to evaluate the effectiveness and safety of a novel buffered riboflavin solution approved for corneal cross-linking (CXL) in progressive keratoconus and secondary corneal ectasia. Following the in vivo preclinical study performed on New Zealand rabbits comparing the [...] Read more.

The purpose of this study was to evaluate the effectiveness and safety of a novel buffered riboflavin solution approved for corneal cross-linking (CXL) in progressive keratoconus and secondary corneal ectasia. Following the in vivo preclinical study performed on New Zealand rabbits comparing the novel 0.25% riboflavin solution (Safecross^®) containing 1% hydroxypropyl methylcellulose (HPMC) with a 0.1% riboflavin solution containing 0.10% EDTA, accelerated epithelium-off CXL was performed on 10 patients (10 eyes treated, with the contralateral eye used as control) through UV-A at a power setting of 9 mW/cm² with a total dose of 5.4 J/cm². Re-epithelialization was evaluated in the postoperative 7 days by fluorescein dye test at biomicroscopy; endothelial cell count and morphology (ECD) were analyzed by specular microscopy at the 1st and 6th month of follow-up and demarcation line depth (DLD) measured by anterior segment optical coherence tomography (AS-OCT) one month after the treatment. We observed complete re-epithelization in all eyes between 72 and 96 h after surgery (88 h on average). ECD and morphology remained unchanged in all eyes. DLD was detected at a mean depth of 362 ± 50 µm, 20% over solutions with equivalent dosage. SafeCross^® riboflavin solution chemically-boosted corneal cross-linking seems to optimize CXL oxidative reaction by higher superoxide anion release, improving DLD by a factor of 20%, without adverse events for corneal endothelium. Full article

(This article belongs to the Special Issue Innovations in Keratoconus Diagnosis and Management)

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9 pages, 1502 KiB

Open AccessArticle

Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning

by Naoko Kato, Hiroki Masumoto, Mao Tanabe, Chikako Sakai, Kazuno Negishi, Hidemasa Torii, Hitoshi Tabuchi and Kazuo Tsubota

J. Clin. Med. 2021, 10(4), 844; https://doi.org/10.3390/jcm10040844 - 18 Feb 2021

Cited by 18 | Viewed by 3064

Abstract

We aimed to predict keratoconus progression and the need for corneal crosslinking (CXL) using deep learning (DL). Two hundred and seventy-four corneal tomography images taken by Pentacam HR^® (Oculus, Wetzlar, Germany) of 158 keratoconus patients were examined. All patients were examined two [...] Read more.

We aimed to predict keratoconus progression and the need for corneal crosslinking (CXL) using deep learning (DL). Two hundred and seventy-four corneal tomography images taken by Pentacam HR^® (Oculus, Wetzlar, Germany) of 158 keratoconus patients were examined. All patients were examined two times or more, and divided into two groups; the progression group and the non-progression group. An axial map of the frontal corneal plane, a pachymetry map, and a combination of these two maps at the initial examination were assessed according to the patients’ age. Training with a convolutional neural network on these learning data objects was conducted. Ninety eyes showed progression and 184 eyes showed no progression. The axial map, the pachymetry map, and their combination combined with patients’ age showed mean AUC values of 0.783, 0.784, and 0.814 (95% confidence interval (0.721–0.845) (0.722–0.846), and (0.755–0.872), respectively), with sensitivities of 87.8%, 77.8%, and 77.8% ((79.2–93.7), (67.8–85.9), and (67.8–85.9)) and specificities of 59.8%, 65.8%, and 69.6% ((52.3–66.9), (58.4–72.6), and (62.4–76.1)), respectively. Using the proposed DL neural network model, keratoconus progression can be predicted on corneal tomography maps combined with patients’ age. Full article

(This article belongs to the Special Issue Innovations in Keratoconus Diagnosis and Management)

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Review

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19 pages, 1879 KiB

Open AccessReview

Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis

by Ke Cao, Karin Verspoor, Srujana Sahebjada and Paul N. Baird

J. Clin. Med. 2022, 11(3), 478; https://doi.org/10.3390/jcm11030478 - 18 Jan 2022

Cited by 10 | Viewed by 2401

Abstract

(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accuracy (as determined by pooled sensitivity and specificity) in detecting keratoconus (KC), and measure reporting completeness of machine learning models in KC [...] Read more.

(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accuracy (as determined by pooled sensitivity and specificity) in detecting keratoconus (KC), and measure reporting completeness of machine learning models in KC based on TRIPOD (the transparent reporting of multivariable prediction models for individual prognosis or diagnosis) statement. (2) Methods: Two independent reviewers searched the electronic databases for all potential articles on machine learning and KC published prior to 2021. The TRIPOD 29-item checklist was used to evaluate the adherence to reporting guidelines of the studies, and the adherence rate to each item was computed. We conducted a meta-analysis to determine the pooled sensitivity and specificity of machine learning models for detecting KC. (3) Results: Thirty-five studies were included in this review. Thirty studies evaluated machine learning models for detecting KC eyes from controls and 14 studies evaluated machine learning models for detecting early KC eyes from controls. The pooled sensitivity for detecting KC was 0.970 (95% CI 0.949–0.982), with a pooled specificity of 0.985 (95% CI 0.971–0.993), whereas the pooled sensitivity of detecting early KC was 0.882 (95% CI 0.822–0.923), with a pooled specificity of 0.947 (95% CI 0.914–0.967). Between 3% and 48% of TRIPOD items were adhered to in studies, and the average (median) adherence rate for a single TRIPOD item was 23% across all studies. (4) Conclusions: Application of machine learning model has the potential to make the diagnosis and monitoring of KC more efficient, resulting in reduced vision loss to the patients. This review provides current information on the machine learning models that have been developed for detecting KC and early KC. Presently, the machine learning models performed poorly in identifying early KC from control eyes and many of these research studies did not follow established reporting standards, thus resulting in the failure of these clinical translation of these machine learning models. We present possible approaches for future studies for improvement in studies related to both KC and early KC models to more efficiently and widely utilize machine learning models for diagnostic process. Full article

(This article belongs to the Special Issue Innovations in Keratoconus Diagnosis and Management)

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18 pages, 4583 KiB

Open AccessFeature PaperReview

Corneal Cross-Linking for Paediatric Keratoconus: A Systematic Review and Meta-Analysis

by Hidenaga Kobashi, Osamu Hieda, Motohiro Itoi, Kazutaka Kamiya, Naoko Kato, Jun Shimazaki, Kazuo Tsubota and the Keratoconus Study Group of Japan

J. Clin. Med. 2021, 10(12), 2626; https://doi.org/10.3390/jcm10122626 - 15 Jun 2021

Cited by 11 | Viewed by 2577

Abstract

All corneal cross-linking techniques attenuated disease progression in patients with pediatric keratoconus for at least one year based on a meta-analysis. A standard and accelerated technique led to marked improvement in visual acuity. We determined the efficacy and safety of corneal cross-linking (CXL) [...] Read more.

All corneal cross-linking techniques attenuated disease progression in patients with pediatric keratoconus for at least one year based on a meta-analysis. A standard and accelerated technique led to marked improvement in visual acuity. We determined the efficacy and safety of corneal cross-linking (CXL) in pediatric keratoconus by conducting a systematic review and meta-analysis. The PubMed and Cochrane databases were searched for relevant studies on the effects of standard, transepithelial, and/or accelerated CXL protocols in patients aged 18 years or younger. Standardized mean differences with 95% confidence intervals were calculated to compare the data collected at baseline and 12 months. The primary outcomes were maximum keratometry (Kmax) and uncorrected visual acuity (UCVA), and the secondary outcomes were the thinnest corneal thickness (TCT), best-corrected visual acuity (BCVA), and manifest refraction spherical equivalent or cylindrical refraction. Our search yielded 7913 publications, of which 26 were included in our systematic review and 21 were included in the meta-analysis. Standard CXL significantly improved the Kmax, UCVA, and BCVA, and significantly decreased the TCT. Accelerated CXL significantly improved UCVA and BCVA. In the transepithelial and accelerated-transepithelial CXL methods, each measurable parameter did not change after treatments. All CXL techniques attenuated disease progression in patients with pediatric keratoconus for at least one year. Standard and accelerated CXL led to marked improvement in visual acuity. Full article

(This article belongs to the Special Issue Innovations in Keratoconus Diagnosis and Management)

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