Delay in seeking medical services is common in elderly populations, which leads to disease progression and life difficulty. This study aims to assess the prevalence of delay in medical visits and ...treatment and define associated effects and factors in patients with senile cataract, which may help obtain a better understanding of late-life psychopathology and provide the basis for interventions. Patients aged more than 60 years were prospectively recruited in Zhongshan Ophthalmic Center (ZOC). All participants were diagnosed with binocular senile cataract and decided to have primary surgery in ZOC. The distributions of the popularity of delaying outpatient visits and treatment, the degrees of visual impairment, the influences on quality of life, and the reasons for delaying treatment among participants were accessed by the descriptive statistics. Factors associated with the perceptions of cataract treatment were accessed using a binary logistic regression model. A total of 400 senile patients aged from 60 to 94 years were enrolled. At diagnosis, 82 (20.5%) participants had a low vision with monocular acuity of both eyes below 0.05. All participants have felt that their normal lives were affected, and 64 (16%) participants felt that their lives were affected severely. Only 17 (4.25%) participants have sought for medical services immediately after feeling vision loss, and 294 (73.50%) participants have felt vision loss since a year ago before seeking medical help. A total of 298 (74.50%) participants have delayed the surgery time, and 229 (57.25%) patients delayed it for more than 12 months. There were 147 (36.75%) participants delaying surgery on account of no knowledge about it and 114 (28.50%) participants delaying surgery because of fear. There are a high proportion of elderly patients with senile cataract delaying their outpatient visits and surgery treatment, whose normal lives were severely affected. Increasing medical service propaganda about cataract and other common diseases in elderly populations would probably be helpful for improving perceptions of diseases and decreasing medical delays. Public needs to draw more attention to the healthy and medical status of the elderly ocular patients.
•The healthcare workers and non-healthcare workers exhibited perceptional differences regarding safety, validity, trust, and expectations of the implementation of medical AI, in addition to ...differences in demands about desired improvements to AI.•The current achievements of medical AI have catered to the public and won their approval, which is noteworthy given the high level of receptivity and demands expressed by the public.•There is a very large gap between public demands and current achievements.
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.
Abstract
Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. ...Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R
2
> 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (
p
< 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.
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.
Artificial intelligence (AI) has reformed the healthcare system with its compelling capabilities of processing biomedical data for disease diagnosis, prediction, and individualized management. The ...eye, as a non‐invasive observation window for many systemic diseases, can be used to detect the signs of chronic kidney diseases, and other diseases like hypertension and type 2 diabetes mellitus, based on specific manifestations of retinal images. Recent advances using AI technology have posed a great potential of using retinal images for rapid mass screening and prognosis prediction of kidney diseases. Herein, we outlined the key applications of AI in ophthalmology and the detection of systemic diseases based on retinal imaging, especially the current progress of retinal image‐based AI models for the detection and prediction of kidney diseases. We hope to shed light on the current opportunities and future challenges in this field to provide suggestions for further improvement and applications.
In this review, the authors have concentrated on the novel strategies for applying retinal‐image‐based artificial intelligence models in ophthalmology and based on the close relationship between eye and systemic diseases, to diagnose and predict the prognosis of kidney diseases. They have also discussed the recent developments and possible challenges for a wide range of clinical applications in the future.
Abstract
Artificial intelligence (AI) based on deep learning has shown excellent diagnostic performance in detecting various diseases with good-quality clinical images. Recently, AI diagnostic ...systems developed from ultra-widefield fundus (UWF) images have become popular standard-of-care tools in screening for ocular fundus diseases. However, in real-world settings, these systems must base their diagnoses on images with uncontrolled quality (“passive feeding”), leading to uncertainty about their performance. Here, using 40,562 UWF images, we develop a deep learning–based image filtering system (DLIFS) for detecting and filtering out poor-quality images in an automated fashion such that only good-quality images are transferred to the subsequent AI diagnostic system (“selective eating”). In three independent datasets from different clinical institutions, the DLIFS performed well with sensitivities of 96.9%, 95.6% and 96.6%, and specificities of 96.6%, 97.9% and 98.8%, respectively. Furthermore, we show that the application of our DLIFS significantly improves the performance of established AI diagnostic systems in real-world settings. Our work demonstrates that “selective eating” of real-world data is necessary and needs to be considered in the development of image-based AI systems.
Cerebrovascular disease (CeVD) is one of the leading global causes of death and severe disability. To date, retinal microangiopathy has become a reflection of cerebral microangiopathy, mirroring the ...vascular pathological modifications
. To evaluate the retinal structure and microvasculature in patients with CeVD, we conducted a cross-sectional study in Zhongshan Ophthalmic Center and Department of Neurology of Third Affiliated Hospital, Sun Yat-sen University using optical coherence tomography angiography (OCTA). CeVD patients (
= 121; 238 eyes) and healthy controls (
= 44; 57 eyes) were included in the analysis. The CeVD group showed significant thinning of the peripapillary retinal nerve fiber layer (pRNFL) thickness in the temporal and nasal quadrants, and thinning of the macular ganglion cell-inner plexiform layer (GC-IPL) in the inferior quadrant, while macular microvasculature reduction was prominent in all nine quadrants. There were significant correlations between OCTA parameters, visual acuity, and transcranial doppler parameters in the CeVD group. The specific structural parameters combining microvasculature indices showed the best diagnostic accuracies (AUC = 0.918) to discriminate CeVD group from healthy controls. To conclude, we proved that OCTA reveals specific patterns of retinal structural changes and extensive macular microvascular changes in CeVD. Additionally, these retinal abnormalities could prove useful disease biomarkers in the management of individuals at high risk of debilitating complications from a cerebrovascular event.
Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially ...before macular involvement. Manual retinal detachment screening is time-consuming and labour-intensive, which is difficult for large-scale clinical applications. In this study, we developed a cascaded deep learning system based on the ultra-widefield fundus images for automated retinal detachment detection and macula-on/off retinal detachment discerning. The performance of this system is reliable and comparable to an experienced ophthalmologist. In addition, this system can automatically provide guidance to patients regarding appropriate preoperative posturing to reduce retinal detachment progression and the urgency of retinal detachment repair. The implementation of this system on a global scale may drastically reduce the extent of vision impairment resulting from retinal detachment by providing timely identification and referral.
Fundus fluorescein angiography (FFA) examinations are widely used in the evaluation of fundus disease conditions to facilitate further treatment suggestions. Here, we present a protocol for ...performing deep learning-based FFA image analytics with classification and segmentation tasks. We describe steps for data preparation, model implementation, statistical analysis, and heatmap visualization. The protocol is applicable in Python using customized data and can achieve the whole process from diagnosis to treatment suggestion of ischemic retinal diseases.
For complete details on the use and execution of this protocol, please refer to Zhao et al.1
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•Protocol for multi-tasking model development and visualization based on deep learning•Steps for diagnosing common ischemic retinal diseases using a classification task•Steps for detecting ischemic retinal disease lesion area using a segmentation task
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Fundus fluorescein angiography (FFA) examinations are widely used in the evaluation of fundus disease conditions to facilitate further treatment suggestions. Here, we present a protocol for performing deep learning-based FFA image analytics with classification and segmentation tasks. We describe steps for data preparation, model implementation, statistical analysis, and heatmap visualization. The protocol is applicable in Python using customized data and can achieve the whole process from diagnosis to treatment suggestion of ischemic retinal diseases.
To predict central serous chorioretinopathy (CSC) recurrence 3 and 6 months after laser treatment by using machine learning.
Clinical and imaging features of 461 patients (480 eyes) with CSC were ...collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The ZOC data (416 eyes of 401 patients) were used as the training dataset and the internal test dataset, while the XEC data (64 eyes of 60 patients) were used as the external test dataset. Six different machine learning algorithms and an ensemble model were trained to predict recurrence in patients with CSC. After completing the initial detailed investigation, we designed a simplified model using only clinical data and OCT features.
The ensemble model exhibited the best performance among the six algorithms, with accuracies of 0.941 (internal test dataset) and 0.970 (external test dataset) at 3 months and 0.903 (internal test dataset) and 1.000 (external test dataset) at 6 months. The simplified model showed a comparable level of predictive power.
Machine learning achieves high accuracies in predicting the recurrence of CSC patients. The application of an intelligent recurrence prediction model for patients with CSC can potentially facilitate recurrence factor identification and precise individualized interventions.
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