Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China.

Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.

Intraoperative OCT for the assessment of posterior capsular integrity in pediatric cataract surgery.

PURPOSE: To study the morphology of the posterior lens cortex and posterior capsules (PCs) in pediatric patients with posterior lens opacities using intraoperative optical coherence tomography (iOCT). SETTING: Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China. DESIGN: Prospective observational study. METHODS: Pediatric patients with posterior lens opacities were imaged using iOCT during cataract surgery. The morphology of the posterior lens cortex and PC, along with the common patterns to indicate PC integrity, was assessed. Moreover, PC rent during surgery was observed. RESULTS: A total of 62 eyes from 53 patients were included. The mean age of patients was 3.8 years. 4 morphological variants of posterior lens opacity were observed: type I (34/62 [54.8%]) with an intact PC; type II (20/62 [32.3%]) with an intact PC, which protruded into the anterior vitreous; type III (3/62 [4.8%]) with a deficient PC and an inability to delineate the PC; and type IV (5/62 [8.1%]) with dense opacity and an inability to characterize the posterior cortex and PC. Phacoemulsification could be performed in types I and II. In types III and IV, manual nucleus removal was performed instead of phacoemulsification. 3 cases (100%) of type III PC dehiscence developed during surgery, whereas no cases developed PC dehiscence of other types. CONCLUSIONS: The morphology of the PC and posterior lens cortex in pediatric posterior lens opacities could be categorized, and PC integrity could be assessed using iOCT, which was useful to guide surgical strategies and increase safety in pre-existing PC dehiscence in pediatric cataract surgery.

Application of Surgical Decision Model for Patients With Childhood Cataract: A Study Based on Real World Data.

Reliable validated methods are necessary to verify the performance of diagnosis and therapy-assisted models in clinical practice. However, some validated results have research bias and may not reflect the results of real-world application. In addition, the conduct of clinical trials has executive risks for the indeterminate effectiveness of models and it is challenging to finish validated clinical trials of rare diseases. Real world data (RWD) can probably solve this problem. In our study, we collected RWD from 251 patients with a rare disease, childhood cataract (CC) and conducted a retrospective study to validate the CC surgical decision model. The consistency of the real surgical type and recommended surgical type was 94.16%. In the cataract extraction (CE) group, the model recommended the same surgical type for 84.48% of eyes, but the model advised conducting cataract extraction and primary intraocular lens implantation (CE + IOL) surgery in 15.52% of eyes, which was different from the real-world choices. In the CE + IOL group, the model recommended the same surgical type for 100% of eyes. The real-recommended matched rates were 94.22% in the eyes of bilateral patients and 90.38% in the eyes of unilateral patients. Our study is the first to apply RWD to complete a retrospective study evaluating a clinical model, and the results indicate the availability and feasibility of applying RWD in model validation and serve guidance for intelligent model evaluation for rare diseases.

Post-keratoplasty Infectious Keratitis: Epidemiology, Risk Factors, Management, and Outcomes.

Post-keratoplasty infectious keratitis (PKIK) represents a unique clinical entity that often poses significant diagnostic and therapeutic challenges. It carries a high risk of serious complications such as graft rejection and failure, and less commonly endophthalmitis. Topical corticosteroids are often required to reduce the risk of graft rejection but their use in PKIK may act as a double-edged sword, particularly in fungal infection. The increased uptake in lamellar keratoplasty in the recent years has also led to complications such as graft-host interface infectious keratitis (IIK), which is particularly difficult to manage. The reported incidence of PKIK differs considerably across different countries, with a higher incidence observed in developing countries (9.2-11.9%) than developed countries (0.02-7.9%). Common risk factors for PKIK include the use of topical corticosteroids, suture-related problems, ocular surface diseases and previous corneal infection. PKIK after penetrating keratoplasty or (deep) anterior lamellar keratoplasty is most commonly caused by ocular surface commensals, particularly Gramme-positive bacteria, whereas PKIK after endothelial keratoplasty is usually caused by Candida spp. Empirical broad-spectrum antimicrobial treatment is the mainstay of treatment for both PKIK, though surgical interventions are required in medically refractory cases (during the acute phase) and those affected by visually significant scarring (during the late phase). In this paper, we aim to provide a comprehensive overview on PKIK, encompassing the epidemiology, risk factors, causes, management and outcomes, and to propose a treatment algorithm for systematically managing this challenging condition.

Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks.

BACKGROUND: Lens opacity seriously affects the visual development of infants. Slit-illumination images play an irreplaceable role in lens opacity detection; however, these images exhibited varied phenotypes with severe heterogeneity and complexity, particularly among pediatric cataracts. Therefore, it is urgently needed to explore an effective computer-aided method to automatically diagnose heterogeneous lens opacity and to provide appropriate treatment recommendations in a timely manner. METHODS: We integrated three different deep learning networks and a cost-sensitive method into an ensemble learning architecture, and then proposed an effective model called CCNN-Ensemble [ensemble of cost-sensitive convolutional neural networks (CNNs)] for automatic lens opacity detection. A total of 470 slit-illumination images of pediatric cataracts were used for training and comparison between the CCNN-Ensemble model and conventional methods. Finally, we used two external datasets (132 independent test images and 79 Internet-based images) to further evaluate the model's generalizability and effectiveness. RESULTS: Experimental results and comparative analyses demonstrated that the proposed method was superior to conventional approaches and provided clinically meaningful performance in terms of three grading indices of lens opacity: area (specificity and sensitivity; 92.00% and 92.31%), density (93.85% and 91.43%) and opacity location (95.25% and 89.29%). Furthermore, the comparable performance on the independent testing dataset and the internet-based images verified the effectiveness and generalizability of the model. Finally, we developed and implemented a website-based automatic diagnosis software for pediatric cataract grading diagnosis in ophthalmology clinics. CONCLUSIONS: The CCNN-Ensemble method demonstrates higher specificity and sensitivity than conventional methods on multi-source datasets. This study provides a practical strategy for heterogeneous lens opacity diagnosis and has the potential to be applied to the analysis of other medical images.

Implementation of artificial intelligence in medicine: Status analysis and development suggestions.

The general public's attitudes, demands, and expectations regarding medical AI could provide guidance for the future development of medical AI to satisfy the increasing needs of doctors and patients. The objective of this study is to investigate public perceptions, receptivity, and demands regarding the implementation of medical AI. An online questionnaire was designed to investigate the perceptions, receptivity, and demands of general public regarding medical AI between October 13 and October 30, 2018. The distributions of the current achievements, public perceptions, receptivity, and demands among individuals in different lines of work (i.e., healthcare vs non-healthcare) and different age groups were assessed by performing descriptive statistics. The factors associated with public receptivity of medical AI were assessed using a linear regression model. In total, 2,780 participants from 22 provinces were enrolled. Healthcare workers accounted for 54.3 % of all participants. There was no significant difference between the healthcare workers and non-healthcare workers in the high proportion (99 %) of participants expressing acceptance of AI (p = 0.8568), but remarkable distributional differences were observed in demands (p < 0.001 for both demands for AI assistance and the desire for AI improvements) and perceptions (p < 0.001 for safety, validity, trust, and expectations). High levels of receptivity (approximately 100 %), demands (approximately 80 %), and expectations (100 %) were expressed among different age groups. The receptivity of medical AI among the non-healthcare workers was associated with gender, educational qualifications, and demands and perceptions of AI. There was a very large gap between current availability of and public demands for intelligence services (p < 0.001). More than 90 % of healthcare workers expressed a willingness to devote time to learning about AI and participating in AI research. The public exhibits a high level of receptivity regarding the implementation of medical AI. To date, the achievements have been rewarding, and further advancements are required to satisfy public demands. There is a strong demand for intelligent assistance in many medical areas, including imaging and pathology departments, outpatient services, and surgery. More contributions are imperative to facilitate integrated and advantageous implementation in medical AI.

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

BACKGROUND: Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial. METHODS: In this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient. RESULTS: Qualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method. CONCLUSION: Our study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.

Patient participation in free cataract surgery: a cross-sectional study of the low-income elderly in urban China.

OBJECTIVES: To explore the characteristics of the low-income elderly who underwent free cataract surgery and to determine the degree of patient satisfaction with the free cataract surgery programme in urban China. METHODS: A free cataract surgery management workflow was designed as a poverty relief project in Guangzhou. In this study, participants who underwent free cataract surgery between January and August 2014 received a telephone interview based on a structured questionnaire. Data were collected on patient demographics, resources, health conditions, reasons for undergoing the free surgery and overall evaluation of the free cataract surgery programme. RESULTS: Among the 833 participants, the mean surgical age was 76.85±7.46 years (95% CI 76.34 to 77.36), and the male to female ratio was 385:448. The majority (94.31%, 746/791) of patients resided in the main urban districts. Patients underwent surgery 61.08±60.15 months (95% CI 56.17 to 66.00) after becoming aware of the cataract, although 66.83% of them reported that their daily lives were influenced by cataracts. Only 21.5% of the respondents underwent physical examinations that included regular eye screening, and only 6.30% were highly educated patients. Financial problems were the primary reason cited by patients for participating in the free surgery programme. Those patients with a monthly family income of 1000-2999¥ (US$161-482) per capita constituted the largest patient population. The free clinics in the parks and the free cataract surgery were highly rated (9.46 and 9.11 of 10 points) by the beneficiaries. CONCLUSIONS: The telephone survey revealed a high level of patient satisfaction regarding the free cataract surgery programme. Most of the patients who participated in the programme resided in major urban districts and had poor health awareness and a low level of education. The information provided by this study is crucial for improving and expanding the management of free cataract surgery programmes. TRIAL REGISTRATION NUMBER: NCT02633865; Post-results.