Comparison of early visual outcomes after SMILE using VISUMAX 800 and VISUMAX 500 for myopia: a retrospective matched case-control study.

VISUMAX 800 was introduced to improve the patient experience and clinical outcomes of small incision lenticule extraction (SMILE). This was a retrospective, matched, and case-control study (1:2) controlled for preoperative central corneal thickness and refractive error that compared early refractive and visual outcomes after SMILE using VISUMAX 800 and VISUMAX 500 to treat myopia. We included 50 eyes that underwent the VISUMAX 800 SMILE and 100 eyes that underwent the VISUMAX 500 SMILE. SMILE using VISUMAX 800 was performed using the CentraLign aid for vertex centration. Cyclotorsion was controlled by an OcuLign assistant in the VISUMAX 800 group after corneal marking. Corneal higher-order aberrations (HOAs) were evaluated using a Pentacam 1 month after surgery. No differences were observed in the pre- and post-operative refractive and visual outcomes at 1 day, 1 month, and 6 months after surgery. VISUMAX 800 induced less total HOAs than VISUMAX 500 (P = 0.036). No statistically significant differences were observed in the amounts of induced spherical aberrations or vertical and horizontal comas. No differences were observed in the 1 month and 6 months refractive and visual outcomes between two SMILE procedures, except for VISUMAX 800, which resulted in lower postoperative total HOAs than VISUMAX 500.

Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.

BACKGROUND: The epiretinal membrane (ERM) is a common retinal disorder characterized by abnormal fibrocellular tissue at the vitreomacular interface. Most patients with ERM are asymptomatic at early stages. Therefore, screening for ERM will become increasingly important. Despite the high prevalence of ERM, few deep learning studies have investigated ERM detection in the color fundus photography (CFP) domain. In this study, we built a generative model to enhance ERM detection performance in the CFP. METHODS: This deep learning study retrospectively collected 302 ERM and 1,250 healthy CFP data points from a healthcare center. The generative model using StyleGAN2 was trained using single-center data. EfficientNetB0 with StyleGAN2-based augmentation was validated using independent internal single-center data and external datasets. We randomly assigned healthcare center data to the development (80%) and internal validation (20%) datasets. Data from two publicly accessible sources were used as external validation datasets. RESULTS: StyleGAN2 facilitated realistic CFP synthesis with the characteristic cellophane reflex features of the ERM. The proposed method with StyleGAN2-based augmentation outperformed the typical transfer learning without a generative adversarial network. The proposed model achieved an area under the receiver operating characteristic (AUC) curve of 0.926 for internal validation. AUCs of 0.951 and 0.914 were obtained for the two external validation datasets. Compared with the deep learning model without augmentation, StyleGAN2-based augmentation improved the detection performance and contributed to the focus on the location of the ERM. CONCLUSIONS: We proposed an ERM detection model by synthesizing realistic CFP images with the pathological features of ERM through generative deep learning. We believe that our deep learning framework will help achieve a more accurate detection of ERM in a limited data setting.

Association between cataract and fatty liver diseases from a nationwide cross-sectional study in South Korea.

This study examined the link between fatty liver disease (FLD) and cataracts, as previous research has suggested that FLD may contribute to metabolic syndrome, systemic inflammation, and potentially cataracts. We studied a nationwide cross-sectional cohort of the Fifth Korean National Health and Nutrition Examination Survey 2010-2011. FLD was defined as nonalcoholic FLD (NAFLD) and metabolic dysfunction-associated FLD (MAFLD). Multinomial logistic regression was utilized to investigate the relationship between cataracts and FLD after adjustment for potential confounders. Participants with cataracts had higher liver fibrosis scores, including the NAFLD fibrosis score (NFS; P < 0.001), fibrosis-4 index (FIB4; P < 0.001), and fatty liver index (FLI; P = 0.001). NAFLD was not associated with a higher odds ratio (OR) for cataracts in the fully adjusted model (OR = 1.23, P = 0.058). MAFLD was significantly associated with a higher OR (OR = 1.34, P = 0.006). After adjusting for all factors, the severity of FLD was linked to an increased risk of cataracts, with significant linear trends (P values for linear trends of NFS, FIB4, and FLI < 0.05). After adjusting for well-known cataract risk factors, MAFLD was significantly associated with cataracts. Our analysis suggests that FLD may serve as an independent risk factor for cataracts.

Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era.

Recently, fundus photography (FP) is being increasingly used. Corneal curvature is an essential factor in refractive errors and is associated with several pathological corneal conditions. As FP-based examination systems have already been widely distributed, it would be helpful for telemedicine to extract information such as corneal curvature using FP. This study aims to develop a deep learning model based on FP for corneal curvature prediction by categorizing corneas into steep, regular, and flat groups. The EfficientNetB0 architecture with transfer learning was used to learn FP patterns to predict flat, regular, and steep corneas. In validation, the model achieved a multiclass accuracy of 0.727, a Matthews correlation coefficient of 0.519, and an unweighted Cohen's κ of 0.590. The areas under the receiver operating characteristic curves for binary prediction of flat and steep corneas were 0.863 and 0.848, respectively. The optic nerve and its peripheral areas were the main focus of the model. The developed algorithm shows that FP can potentially be used as an imaging modality to estimate corneal curvature in the post-COVID-19 era, whereby patients may benefit from the detection of abnormal corneal curvatures using FP in the telemedicine setting.

Modification of mixed-nitrogen anions configuration for accelerating lithium ions transport in the LiFePO4 electrode.

Olivine-type LiFePO4 (LFP) is considered a promising cathode material for lithium-ion batteries (LIBs) owing to its abundance, high specific capacity, and cycling performance. However, its poor electronic and ionic transportation properties degrade the high rate capability, which limits its use in high-energy-density LIBs for applications such as electric vehicles. Therefore, in this study, we propose a modification of the anion configuration through nitrogen substitution using ion implantation to improve electronic and ionic transport during lithiation/delithiation. We found that nitrogen substitution at the oxygen sites effectively improved the electrochemical properties through surface modification and charge-transfer kinetics. In particular, the increased amount of nitrogen substitution at the surface regions resulted in reduced ionic and electronic resistances. These modified characteristics led to a remarkable rate capability with a high capacity (128.2 mA h g-1 at 10C). We expect that these modified anion effects on the electrochemical properties can be effective in the design of cathode materials for LIBs.

Comparison of implantation of posterior chamber phakic IOL implantation and laser vision correction in terms of corneal endothelial cells: 3-year observational paired-eye study.

PURPOSE: To compare the postoperative endothelial cell counts of EVO-implantable collamer lenses (ICLs) with a central hole (V4c and V5) and laser vision correction surgery (laser in situ keratomileusis or photorefractive keratectomy). SETTING: B&VIIT Eye Center, Seoul, South Korea. DESIGN: Retrospective observational and paired contralateral study. METHODS: 62 eyes of 31 patients who underwent EVO-ICLs with a central hole implantation in one eye (phakic intraocular lens [pIOL] group) and laser vision correction in the contralateral eye (LVC group) to correct refractive errors were retrospectively reviewed. Central endothelial cell density (ECD), percentage of hexagonal cells (HEX), coefficient of variation (CoV) in cell size, and adverse events were evaluated for at least 3 years. The endothelial cells were observed using a noncontact specular microscope. RESULTS: All surgeries were performed, without complications during the follow-up period. The mean ECD loss values compared with the preoperative measurements were 6.65% and 4.95% during the 3 years after pIOL and LVC, respectively. There was no significant difference in ECD loss compared with the preoperative values (paired t test, P = .188) between the 2 groups. No significant loss in ECD was observed at any timepoint. The pIOL group showed higher HEX ( P = .018) and lower CoV ( P = .006) values than the LVC group at the last visit. CONCLUSIONS: According to the authors' experience, the EVO-ICL with a central hole implantation was a safe and stable vision correction method. Moreover, it did not induce statistically significant changes in ECD at 3 years postoperatively compared with LVC. However, further long-term follow-up studies are required to confirm these results.

Development of an implantable collamer lens sizing model: a retrospective study using ANTERION swept-source optical coherence tomography and a literature review.

BACKGROUND: Optimal sizing for phakic intraocular lens (EVO-ICL with KS-AquaPort) implantation plays an important role in preventing postoperative complications. We aimed to formulate optimal lens sizing using ocular biometric parameters measured with a Heidelberg anterior segment optical coherence tomography (AS-OCT) device. METHODS: We retrospectively analyzed 892 eyes of 471 healthy subjects treated with an intraocular collamer lens (ICL) and assigned them to either the development (80%) or validation (20%) set. We built vault prediction models using the development set via classic linear regression methods as well as partial least squares and least absolute shrinkage and selection operator (LASSO) regression techniques. We evaluated prediction abilities based on the Bayesian information criterion (BIC) to select the best prediction model. The performance was measured using Pearson's correlation coefficient and the mean squared error (MAE) between the achieved and predicted results. RESULTS: Measurements of aqueous depth (AQD), anterior chamber volume, anterior chamber angle (ACA) distance, spur-to-spur distance, crystalline lens thickness (LT), and white-to-white distance from ANTERION were highly associated with the ICL vault. The LASSO model using the AQD, ACA distance, and LT showed the best BIC results for postoperative ICL vault prediction. In the validation dataset, the LASSO model showed the strongest correlation (r = 0.582, P < 0.001) and the lowest MAE (104.7 µm). CONCLUSION: This is the first study to develop a postoperative ICL vault prediction and lens-sizing model based on the ANTERION. As the measurements from ANTERION and other AS-OCT devices are not interchangeable, ANTERION may be used for optimal ICL sizing using our formula. Because our model was developed based on the East Asian population, further studies are needed to explore the role of this prediction model in different populations.

Predicting Postoperative Anterior Chamber Angle for Phakic Intraocular Lens Implantation Using Preoperative Anterior Segment Metrics.

Purpose: The anterior chamber angle (ACA) is a critical factor in posterior chamber phakic intraocular lens (EVO Implantable Collamer Lens [ICL]) implantation. Herein, we predicted postoperative ACAs to select the optimal ICL size to reduce narrow ACA-related complications. Methods: Regression models were constructed using pre-operative anterior segment optical coherence tomography metrics to predict postoperative ACAs, including trabecular-iris angles (TIAs) and scleral-spur angles (SSAs) at 500 µm and 750 µm from the scleral spur (TIA500, TIA750, SSA500, and SSA750). Data from three expert surgeons were assigned to the development (N = 430 eyes) and internal validation (N = 108 eyes) datasets. Additionally, data from a novice surgeon (N = 42 eyes) were used for external validation. Results: Postoperative ACAs were highly predictable using the machine-learning (ML) technique (extreme gradient boosting regression [XGBoost]), with mean absolute errors (MAEs) of 4.42 degrees, 3.77 degrees, 5.25 degrees, and 4.30 degrees for TIA500, TIA750, SSA500, and SSA750, respectively, in internal validation. External validation also showed MAEs of 3.93 degrees, 3.86 degrees, 5.02 degrees, and 4.74 degrees for TIA500, TIA750, SSA500, and SSA750, respectively. Linear regression using the pre-operative anterior chamber depth, anterior chamber width, crystalline lens rise, TIA, and ICL size also exhibited good performance, with no significant difference compared with XGBoost in the validation sets. Conclusions: We developed linear regression and ML models to predict postoperative ACAs for ICL surgery anterior segment metrics. These will prevent surgeons from overlooking the risks associated with the narrowing of the ACA. Translational Relevance: Using the proposed algorithms, surgeons can consider the postoperative ACAs to increase surgical accuracy and safety.

N+-Implantation on Nb Coating as Protective Layer for Metal Bipolar Plate in PEMFCs and Their Electrochemical Characteristics.

Nitrogen ions were implanted into the coated Nb layer by plasma immersion ion implantation to improve resistance to corrosion of a metal bipolar plate. Due to nitrogen implantation, the corrosion behavior of the Nb layer was enhanced. The electron microscope observation reveals that the microstructure of the Nb layer became denser and had fewer defects with increasing implantation energy. As a result, the densified structure effectively prevented direct contact with the corrosive electrolyte. In addition, at a higher implantation rate (6.40 × 1017 N2/cm2), a thin amorphous layer was formed on the surface, and the implanted nitrogen ions reacted at neighboring Nb sites, resulting in the localized formation of nitrides. Such phase and structural changes contributed to further improve corrosion resistance. In particular, the implanted Nb layer at bias voltage of 10 kV exhibited a current density more than one order of magnitude smaller with a two times faster stabilization than the as-deposited Nb layer under the PEMFC operating conditions.

Enhancement of electrochromic properties using nanostructured amorphous tungsten trioxide thin films.

Electrochromic technologies have recently attracted attention due to their energy-saving performance for reducing green gas emissions. The materials design and preparation of electrochromic materials with sufficient microstructure and crystallographic features for suitable ion intercalation/deintercalation are essential for high performance and efficiency. In the present work, nanostructured amorphous tungsten trioxide (WO3) films are electrodeposited to enhance electrochromic properties by controlling the pH of electrolytes. Electron microscopy and spectroelectrochemical analysis demonstrate that smaller grain sizes result in larger electrochemical reactive surface areas and shorter ion diffusion lengths. Consequently, the ions efficiently intercalated and deintercalated during the coloring and bleaching states, respectively. In particular, prepared WO3 films at electrolyte pH 1.4 demonstrate high optical modulation (74.83%) and good transmittance switching speeds (1.56 and 2.06 s during coloring and bleaching, respectively) at 650 nm, as well as comparable coloration efficiency (61.92 cm2 C-1 at 650 nm).

Comparison of Corneal Higher-Order Aberrations Following Topography-Guided LASIK and SMILE for Myopic Correction: A Propensity Score Matching Analysis.

Ocular aberrations, particularly corneal higher-order aberrations (HOAs), which impair visual quality, should be minimized or corrected during any laser vision correction. We compared changes in visual outcomes, including HOAs, in patients who underwent Topography-Guided laser-assisted in situ keratomileusis (TG-LASIK) or small-incision lenticule extraction (SMILE) after propensity score matching (PSM) to reduce selection bias. Of 2749 patients who underwent SMILE or TG-LASIK for myopia, 152 eyes underwent complete ophthalmic examination preoperatively and over six months postoperatively. Visual outcomes were comparatively analyzed after PSM. As a result, 45 eyes were included in each group after PSM. There was a comparable improvement in visual acuity (VA) and refractive parameters postoperatively, with no difference between the two PSM-groups. However, 6.6% in the SMILE PSM-group lost two or more lines of Snellen VA at the six-month follow-up, while none in the TG-LASIK PSM-group did. Specifically, the SMILE PSM-group showed a significant increase in corneal HOAs, including spherical aberration, coma, and total HOAs (0.0736 ± 0.162 µm; 0.181 ± 0.233 µm; and 0.151 ± 0.178 µm, respectively), whereas TG-LASIK PSM-group did not. Furthermore, SMILE PSM-group had greater postoperative corneal HOAs than those in TG-LASIK PSM-group. Collectively, TG-LASIK induces fewer corneal HOAs even after facilitating between-group comparability using PSM analysis. TG-LASIK provides better visual quality than SMILE for myopia.

Oculomics for sarcopenia prediction: a machine learning approach toward predictive, preventive, and personalized medicine.

Aims: Sarcopenia is characterized by a gradual loss of skeletal muscle mass and strength with increased adverse outcomes. Recently, large-scale epidemiological studies have demonstrated a relationship between several chronic disorders and ocular pathological conditions using an oculomics approach. We hypothesized that sarcopenia can be predicted through eye examinations, without invasive tests or radiologic evaluations in the context of predictive, preventive, and personalized medicine (PPPM/3PM). Methods: We analyzed data from the Korean National Health and Nutrition Examination Survey (KNHANES). The training set (80%, randomly selected from 2008 to 2010) data were used to construct the machine learning models. Internal (20%, randomly selected from 2008 to 2010) and external (from the KNHANES 2011) validation sets were used to assess the ability to predict sarcopenia. We included 8092 participants in the final dataset. Machine learning models (XGBoost) were trained on ophthalmological examinations and demographic factors to detect sarcopenia. Results: In the exploratory analysis, decreased levator function (odds ratio [OR], 1.41; P value <0.001), cataracts (OR, 1.31; P value = 0.013), and age-related macular degeneration (OR, 1.38; P value = 0.026) were associated with an increased risk of sarcopenia in men. In women, an increased risk of sarcopenia was associated with blepharoptosis (OR, 1.23; P value = 0.038) and cataracts (OR, 1.29; P value = 0.010). The XGBoost technique showed areas under the receiver operating characteristic curves (AUCs) of 0.746 and 0.762 in men and women, respectively. The external validation achieved AUCs of 0.751 and 0.785 for men and women, respectively. For practical and fast hands-on experience with the predictive model for practitioners who may be willing to test the whole idea of sarcopenia prediction based on oculomics data, we developed a simple web-based calculator application (https://knhanesoculomics.github.io/sarcopenia) to predict the risk of sarcopenia and facilitate screening, based on the model established in this study. Conclusion: Sarcopenia is treatable before the vicious cycle of sarcopenia-related deterioration begins. Therefore, early identification of individuals at a high risk of sarcopenia is essential in the context of PPPM. Our oculomics-based approach provides an effective strategy for sarcopenia prediction. The proposed method shows promise in significantly increasing the number of patients diagnosed with sarcopenia, potentially facilitating earlier intervention. Through patient oculometric monitoring, various pathological factors related to sarcopenia can be simultaneously analyzed, and doctors can provide personalized medical services according to each cause. Further studies are needed to confirm whether such a prediction algorithm can be used in real-world clinical settings to improve the diagnosis of sarcopenia. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00292-3.

Clinical outcomes of the immediate reapplication of small-incision lenticule extraction without adjusting the surgical parameters after suction loss.

This study was to analyze the clinical outcomes of immediate reapplication of small-incision lenticule extraction (SMILE) without adjusting the surgical parameters after suction loss and to compare the outcomes with contralateral eyes that underwent uneventful SMILE. A total of 74 patients who underwent uneventful SMILE in one eye (Uneventful group) and immediate reapplication of SMILE without adjusting the surgical parameters after suction loss in the contralateral eye (Suction loss group) were included. Suction loss occurred during the posterior lenticule surface cut in 39 eyes (53%) and the cap cut in 35 eyes (47%). Surgical outcomes, including visual acuity, manifest refraction, keratometry, and corneal wavefront aberrations, were evaluated at 6 months postoperatively. The mean uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), and spherical equivalent were - 0.02 ± 0.07, - 0.04 ± 0.04, and - 0.10 ± 0.46 diopters (D), respectively, in the Suction loss group and - 0.02 ± 0.07, - 0.04 ± 0.05, and - 0.19 ± 0.53 D, respectively (P = 0.965, 0.519, and 0.265, respectively), in the Uneventful group. Changes between the preoperative and 6-month postoperative total corneal aberrations, spherical aberrations, and horizontal and vertical coma did not significantly differ between the Suction loss and Uneventful groups. Immediate reapplication of SMILE without adjusting the surgical parameters after suction loss resulted in good surgical outcomes that were comparable with those of uneventful SMILE.

Machine learning predicting myopic regression after corneal refractive surgery using preoperative data and fundus photography.

PURPOSE: Myopic regression after surgery is the most common long-term complication of refractive surgery, but it is difficult to identify myopic regression without long-term observation. This study aimed to develop machine learning models to identify high-risk patients for refractive regression based on preoperative data and fundus photography. METHODS: This retrospective study assigned subjects to the training (n = 1606 eyes) and validation (n = 403 eyes) datasets with chronological data splitting. Machine learning models with ResNet50 (for image analysis) and XGBoost (for integration of all variables and fundus photography) were developed based on subjects who underwent corneal refractive surgery. The primary outcome was the predictive performance for the presence of myopic regression at 4 years of follow-up examination postoperatively. RESULTS: By integrating all factors and fundus photography, the final combined machine learning model showed good performance to predict myopic regression of more than 0.5 D (area under the receiver operating characteristic curve [ROC-AUC], 0.753; 95% confidence interval [CI], 0.710-0.793). The performance of the final model was better than the single ResNet50 model only using fundus photography (ROC-AUC, 0.673; 95% CI, 0.627-0.716). The top-five most important input features were fundus photography, preoperative anterior chamber depth, planned ablation thickness, age, and preoperative central corneal thickness. CONCLUSION: Our machine learning algorithm provides an efficient strategy to identify high-risk patients with myopic regression without additional labor, cost, and time. Surgeons might benefit from preoperative risk assessment of myopic regression, patient counseling before surgery, and surgical option decisions.

A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs.

BACKGROUND AND OBJECTIVES: Patients with angle-closure glaucoma (ACG) are asymptomatic until they experience a painful attack. Shallow anterior chamber depth (ACD) is considered a significant risk factor for ACG. We propose a deep learning approach to detect shallow ACD using fundus photographs and to identify the hidden features of shallow ACD. METHODS: This retrospective study assigned healthy subjects to the training (n = 1188 eyes) and test (n = 594) datasets (prospective validation design). We used a deep learning approach to estimate ACD and build a classification model to identify eyes with a shallow ACD. The proposed method, including subtraction of the input and output images of CycleGAN and a thresholding algorithm, was adopted to visualize the characteristic features of fundus photographs with a shallow ACD. RESULTS: The deep learning model integrating fundus photographs and clinical variables achieved areas under the receiver operating characteristic curve of 0.978 (95% confidence interval [CI], 0.963-0.988) for an ACD ≤ 2.60 mm and 0.895 (95% CI, 0.868-0.919) for an ACD ≤ 2.80 mm, and outperformed the regression model using only clinical variables. However, the difference between shallow and deep ACD classes on fundus photographs was difficult to be detected with the naked eye. We were unable to identify the features of shallow ACD using the Grad-CAM. The CycleGAN-based feature images showed that area around the macula and optic disk significantly contributed to the classification of fundus photographs with a shallow ACD. CONCLUSIONS: We demonstrated the feasibility of a novel deep learning model to detect a shallow ACD as a screening tool for ACG using fundus photographs. The CycleGAN-based feature map showed the hidden characteristic features of shallow ACD that were previously undetectable by conventional techniques and ophthalmologists. This framework will facilitate the early detection of shallow ACD to prevent overlooking the risks associated with ACG.

The Short-term Effects of Hippotherapy and Therapeutic Horseback Riding on Spasticity in Children With Cerebral Palsy: A Meta-analysis.

PURPOSE: We systematically reviewed the short-term effects of hippotherapy and therapeutic horseback riding (THR) on lower-limb muscle spasticity in children with cerebral palsy (CP). METHODS: PubMed, EMBASE, Cochrane Library, and Google Scholar databases were searched for relevant quantitative studies. Treatment effects were coded using the Ashworth scale (AS) or modified Ashworth scale (MAS) in pre- and posttreatment evaluations. Of the 73 studies identified initially, 7 met the inclusion criteria. RESULTS: Treatment was associated with positive effects on lower-limb muscle spasticity, as supported by the AS or MAS scores. However, repeated trials did not show a statistically significant difference from a single trial (Q = 2.95, P = .086). CONCLUSION: Hippotherapy and THR can be used to treat lower-limb muscle spasticity in children with CP. However, repeated sessions did not show a better effect in reducing spasticity. WHAT THIS ADDS TO THE EVIDENCE: This is the first meta-analysis to confirm that hippotherapy or THR can reduce lower-limb muscle spasticity in children with CP in the short term, but long-term effects on function still require further studies.

Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images.

BACKGROUND/OBJECTIVES: This study aimed to evaluate a deep learning model for estimating uncorrected refractive error using posterior segment optical coherence tomography (OCT) images. METHODS: In this retrospective study, we assigned healthy subjects to development (N = 688 eyes of 344 subjects) and test (N = 248 eyes of 124 subjects) datasets (prospective validation design). We developed and validated OCT-based deep learning models to estimate refractive error. A regression model based on a pretrained ResNet50 architecture was trained using horizontal OCT images to predict the spherical equivalent (SE). The performance of the deep learning model for detecting high myopia was also evaluated. A saliency map was generated using the Grad-CAM technique to visualize the characteristic features. RESULTS: The developed model showed a low mean absolute error for SE prediction (2.66 D) and a significant Pearson correlation coefficient of 0.588 (P < 0.001) in the test dataset validation. To detect high myopia, the model yielded an area under the receiver operating characteristic curve of 0.813 (95% confidence interval [CI], 0.744-0.881) and an accuracy of 71.4% (95% CI, 65.3-76.9%). The inner retinal layers and relatively steepened curvatures were highlighted using a saliency map to detect high myopia. CONCLUSION: A deep learning algorithm showed that OCT could potentially be used as an imaging modality to estimate refractive error. This method will facilitate the evaluation of refractive error to prevent clinicians from overlooking the risks associated with refractive error during OCT assessment.

Corrosion Behavior of Niobium-Coated 316L Stainless Steels as Metal Bipolar Plates for Polymer Electrolyte Membrane Fuel Cells.

Niobium was coated on 316L stainless steel by pulsed direct-current (DC) magnetron sputtering to improve corrosion behavior. The applied bias voltage highly affected the microstructure and crystallographic features, which lead to improved corrosion behavior. Due to the increased bias voltage, the microstructure of the niobium coating layer presented a smaller crystallite size and a densified structure, which obviously reduced the number of pinholes in the coated layer. Additionally, an increase in the degree of orientation toward the (110) plane, the most densely packed plane, lead to reduced dissolution of metal ions. Therefore, a pure niobium coating layer effectively protected the metal bipolar plate from a highly corrosive environment of polymer electrolyte membrane fuel cell (PEMFC) stacks. In particular, higher bias voltages of 600 and 800 V induced improved corrosion resistance, which satisfied the demand for the bipolar plate.

Global Trends of Regional Health Information Systems and Suggested Strategic Utilization of their Medical Information.

OBJECTIVE: Along with the exponentially-growing data produced and accumulated every day through mobile platforms, social networking services, the Internet, and other media, information is becoming increasingly important as a strategic resource. This report presents specific and clear directions and suggests empirical project plans regarding innovations in regional health information systems to promote the utilization of medical information. METHODS: We reviewed and examined documents about global trends and examples of regional health information systems. The problems and solutions of health information utilization and regional health information systems in Korea were analyzed. RESULTS: This study presented examples of the establishment of health information systems, problems in the use of local healthcare information, and an empirical project for improvement. CONCLUSIONS: The results of this study imply the need for long-term and systematic approaches for the use of medical information and the establishment of a local healthcare information system, along with implementation plans. As a first step, it is imperative to clarify the goal of building a medical information system, the information that must be provided to build the system, and the data that should be collected to provide such information, while moving away from the mentality of focusing on technology-oriented medical information services. In addition, it is necessary to consider information governance, data-based service development, and the medical innovation framework, which are ways to efficiently manage, utilize, and systemize the data to be collected.

Clinical Practice Guideline for Postoperative Rehabilitation in Older Patients With Hip Fractures.

OBJECTIVE: The incidence of hip fractures is increasing worldwide with the aging population, causing a challenge to healthcare systems due to the associated morbidities and high risk of mortality. After hip fractures in frail geriatric patients, existing comorbidities worsen and new complications are prone to occur. Comprehensive rehabilitation is essential for promoting physical function recovery and minimizing complications, which can be achieved through a multidisciplinary approach. Recommendations are required to assist healthcare providers in making decisions on rehabilitation post-surgery. Clinical practice guidelines regarding rehabilitation (physical and occupational therapies) and management of comorbidities/complications in the postoperative phase of hip fractures have not been developed. This guideline aimed to provide evidence-based recommendations for various treatment items required for proper recovery after hip fracture surgeries. METHODS: Reflecting the complex perspectives associated with rehabilitation post-hip surgeries, 15 key questions (KQs) reflecting the complex perspectives associated with post-hip surgery rehabilitation were categorized into four areas: multidisciplinary, rehabilitation, community-care, and comorbidities/complications. Relevant literature from four databases (PubMed, EMBASE, Cochrane Library, and KoreaMed) was searched for articles published up to February 2020. The evidence level and recommended grade were determined according to the grade of recommendation assessment, development, and evaluation method. RESULTS: A multidisciplinary approach, progressive resistance exercises, and balance training are strongly recommended. Early ambulation, weigh-bearing exercises, activities of daily living training, community-level rehabilitation, management of comorbidities/complication prevention, and nutritional support were also suggested. This multidisciplinary approach reduced the total healthcare cost. CONCLUSION: This guideline presents comprehensive recommendations for the rehabilitation of adult patients after hip fracture surgery.