Diagnosability of Keratoconus Using Deep Learning With Placido Disk-Based Corneal Topography.

Purpose: Placido disk-based corneal topography is still most commonly used in daily practice. This study was aimed to evaluate the diagnosability of keratoconus using deep learning of a color-coded map with Placido disk-based corneal topography. Methods: We retrospectively examined 179 keratoconic eyes [Grade 1 (54 eyes), 2 (52 eyes), 3 (23 eyes), and 4 (50 eyes), according to the Amsler-Krumeich classification], and 170 age-matched healthy eyes, with good quality images of corneal topography measured with a Placido disk corneal topographer (TMS-4TM, Tomey). Using deep learning of a color-coded map, we evaluated the diagnostic accuracy, sensitivity, and specificity, for keratoconus screening and staging tests, in these eyes. Results: Deep learning of color-coded maps exhibited an accuracy of 0.966 (sensitivity 0.988, specificity 0.944) in discriminating keratoconus from normal eyes. It also exhibited an accuracy of 0.785 (0.911 for Grade 1, 0.868 for Grade 2, 0.920 for Grade 3, and 0.905 for Grade 4) in classifying the stage. The area under the curve value was 0.997, 0.955, 0.899, 0.888, and 0.943 as Grade 0 (normal) to 4 grading tests, respectively. Conclusions: Deep learning using color-coded maps with conventional corneal topography effectively distinguishes between keratoconus and normal eyes and classifies the grade of the disease, indicating that this will become an aid for enhancing the diagnosis and staging ability of keratoconus in a clinical setting.

Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps.

BACKGROUND: To predict keratoconus progression using deep learning of the color-coded maps measured with a swept-source anterior segment optical coherence tomography (As-OCT) device. METHODS: We enrolled 218 keratoconic eyes with and without disease progression. Using deep learning of the 6 color-coded maps (anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power, and pachymetry map) obtained by the As-OCT (CASIA, Tomey), we assessed the accuracy, sensitivity, and specificity of prediction of keratoconus progression in such eyes. RESULTS: Deep learning of the 6 color-coded maps exhibited an accuracy of 0.794 in discriminating keratoconus with and without progression. For a single map analysis, posterior elevation map (0.798) showed the highest accuracy, followed by anterior curvature map (0.775), posterior corneal curvature map (0.757), anterior elevation map (0.752), total refractive power map (0.729), and pachymetry map (0.720), in distinguishing between progressive and non-progressive keratoconus. The use of the adjusted algorithm by age subgroups improved to an accuracy of 0.849. CONCLUSIONS: Deep learning of the As-OCT color-coded maps effectively discriminates progressive keratoconus from non-progressive keratoconus with an accuracy of approximately 85% using the adjusted age algorithm, indicating that it will become an aid for predicting the progression of the disease, which is clinically beneficial for decision-making of the surgical indication of corneal cross-linking (CXL).

Effects of video viewing smartphone application intervention involving positive word stimulation in people with subthreshold depression: A pilot randomized controlled trial.

BACKGROUND: A smartphone application (i.e., SPSRS) was developed to help people with subthreshold depression (StD) improve depressive symptoms by presenting positive word stimuli in videos. However, to date, no randomized controlled trials (RCTs) were conducted to investigate SPSRS application interventions for depressive symptoms in people with StD. Therefore, a pilot RCT was conducted to assess the preliminary efficacy of the SPSRS application intervention for people with StD. METHODS: In a pilot RCT, 32 participants (female = 34.4%, mean age = 20.06, SD = 1.24) with StD were randomized to SPSRS application intervention for approximately 10 min/a day for 5 weeks (experimental group; n = 16) or no intervention (wait list control group; n = 16). The primary outcome is the change from baseline in the Center for Epidemiologic Studies Depression Scale (CES-D) score after the 5-week intervention. The secondary outcomes are the change from baseline in the Kessler Screening Scale for Psychological Distress (K-6) score and the Generalized Anxiety Disorder 7-item scale (GAD-7) after the 5-week intervention. RESULTS: No participants dropped out of the study. The experimental group displayed medium, small, and small improvements in CES-D, K-6, and GAD-7 scores (adjusted Hedge's g = -0.64, -0.29, and -0.40), respectively, compared with control. LIMITATIONS: The observed effects must be considered preliminary due to the small sample size. CONCLUSIONS: The results suggest the potential of intervention using the SPSRS application to reduce depressive symptoms in people with StD. Future studies should replicate these findings in a full-scale RCT.

Study protocol for a pilot randomized controlled trial on a smartphone application-based intervention for subthreshold depression: Study protocol clinical trial (SPIRIT Compliant).

INTRODUCTION: Interventions aimed at addressing subthreshold depression (StD) are important to prevent the onset of major depressive disorder. Our video playback application (SPSRS) is designed to reduce depressive symptoms by presenting positive words in videos, shedding new light on the treatment of StD. However, no randomized controlled trial (RCT) has utilized this video playback application for the treatment of individuals with StD. Therefore, a pilot RCT was designed to determine the feasibility of a full-scale trial. We herein present a study protocol for investigating the utility of a video playback application intervention for individuals with StD. METHODS: This 5-week, single-blind, 2-arm, parallel-group, pilot RCT will determine the effectiveness of the video playback application by comparing individuals who had and had not been exposed to the same. A total of 32 individuals with StD will be randomly assigned to the experimental or control group at a 1:1 ratio. The experimental group will receive a 10-minute intervention containing the video playback application per day, whereas the control group will receive no intervention. The primary outcome will include changes in the Center for Epidemiologic Studies Depression Scale score after the 5-week intervention, while secondary outcomes will include changes in the Kessler Screening Scale for psychological distress and the generalized anxiety disorder 7-item scale score after the 5-week intervention. Statistical analysis using linear mixed models with the restricted maximum likelihood estimation method will then be performed. DISCUSSION: This pilot RCT will have been the first to explore the utility of SPSRS application interventions that display positive words in videos for individuals with StD. The results of this pilot trial are expected to help in the design and implementation of a full-scale RCT that investigates the effects of SPSRS applications among individuals with StD. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04136041.

Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study.

OBJECTIVE: To evaluate the diagnostic accuracy of keratoconus using deep learning of the colour-coded maps measured with the swept-source anterior segment optical coherence tomography (AS-OCT). DESIGN: A diagnostic accuracy study. SETTING: A single-centre study. PARTICIPANTS: A total of 304 keratoconic eyes (grade 1 (108 eyes), 2 (75 eyes), 3 (42 eyes) and 4 (79 eyes)) according to the Amsler-Krumeich classification, and 239 age-matched healthy eyes. MAIN OUTCOME MEASURES: The diagnostic accuracy of keratoconus using deep learning of six colour-coded maps (anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power and pachymetry map). RESULTS: Deep learning of the arithmetical mean output data of these six maps showed an accuracy of 0.991 in discriminating between normal and keratoconic eyes. For single map analysis, posterior elevation map (0.993) showed the highest accuracy, followed by posterior curvature map (0.991), anterior elevation map (0.983), corneal pachymetry map (0.982), total refractive power map (0.978) and anterior curvature map (0.976), in discriminating between normal and keratoconic eyes. This deep learning also showed an accuracy of 0.874 in classifying the stage of the disease. Posterior curvature map (0.869) showed the highest accuracy, followed by corneal pachymetry map (0.845), anterior curvature map (0.836), total refractive power map (0.836), posterior elevation map (0.829) and anterior elevation map (0.820), in classifying the stage. CONCLUSIONS: Deep learning using the colour-coded maps obtained by the AS-OCT effectively discriminates keratoconus from normal corneas, and furthermore classifies the grade of the disease. It is suggested that this will become an aid for improving the diagnostic accuracy of keratoconus in daily practice. CLINICAL TRIAL REGISTRATION NUMBER: 000034587.