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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.
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Catarata , Facoemulsificação , Segmento Anterior do Olho , Criança , Pré-Escolar , Humanos , Implante de Lente Intraocular , Tomografia de Coerência Óptica , Acuidade VisualRESUMO
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.
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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.
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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.
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Inteligência Artificial/tendências , Medicina/tendências , Adulto , Fatores Etários , Escolaridade , Feminino , Pessoal de Saúde , Humanos , Masculino , Pessoa de Meia-Idade , Médicos , Opinião Pública , Análise de Regressão , Fatores Sexuais , Fatores Socioeconômicos , Inquéritos e QuestionáriosRESUMO
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.
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Análise Custo-Benefício , Diagnóstico por Computador/economia , Diagnóstico por Imagem , Oftalmopatias/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Automação , SoftwareRESUMO
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±7.46â 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±60.15â 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.