Experimental Study on Gas Loss Derived from Positive Pressure.

Positive pressure sampling enables the fixed-point and rapid acquisition of coal samples, but the derivation of loss volume during sampling is usually based on the law of gas desorption from granular coal at atmospheric pressure, which seriously affects the reasonableness of loss amounts under positive pressure and thus leads to errors in gas content determination. The gas loss under positive pressure is the key to the accurate determination of the gas content of coal seams. To obtain reliable loss data, under different positive pressures, we tested the gas desorption process of anthracite coal samples with different adsorption equilibrium pressures, analyzed the effect of positive pressure on gas desorption, studied the changes in the gas desorption rate caused by positive pressure, recorded the fluctuation of the amount of gas loss, and compared the values of loss under different conditions. The results show that the positive pressure is the main factor affecting gas desorption compared to the adsorption equilibrium pressure. The positive pressure has an inhibitory influence on gas desorption. Under the same positive pressure, the gas desorption rate shows a decreasing trend over time, and at the same time, the gas desorption rate gradually decreases accompanied by the increasing positive pressure. The gas loss error rate increases with increasing adsorption pressure under the same positive pressure. However, under the same adsorption pressure, the error rate of loss quantity presents a significant increase with positive pressure. The relative error of gas loss under different positive pressures can reach 63-180%, and the positive pressure has an obvious influence on gas loss. This study has experimentally confirmed that positive pressure has a greater effect on gas desorption than adsorption pressure, which will theoretically improve the method of deriving the amount of gas loss and will provide a basis for the accurate determination of gas content under positive pressure in engineering terms.

The associations of high academic performance with childhood ametropia prevalence and myopia development in China.

BACKGROUND: To assess associations of high academic performance with ametropia prevalence and myopia development in Chinese schoolchildren. METHODS: This multicohort observational study was performed in Guangdong, China. We first performed a cross-sectional cohort analysis of students in grades 1 to 9 from Yangjiang to evaluate the relationship between academic performance and refractive status on a yearly basis. We also performed longitudinal analyses of students in Shenzhen to evaluate the trend of academic performance with refractive changes over a period of 33 months. All refractive statuses were measured using noncycloplegic autorefractors. RESULTS: A total of 32,360 children with or without myopia were recruited in this study (mean age 10.08 years, 18,360 males and 14,000 females). Cross-sectional cohort analyses in Yangjiang showed that the prevalence of hyperopia was associated with lower academic scores in grade one, the year students entered primary school (ß=-0.04, P=0.01), whereas the prevalence of myopia was associated with higher academic scores in grade six and grade eight, the years in which students were about to take entrance examinations for junior high school or senior high school (ß=0.020, P=0.038; ß=0.041, P=0.002). Longitudinal analysis showed that in Shenzhen, faster myopia development was associated with better scores in all grades even after adjustments for BMI, outdoor activity time, screen time, reading time, and parental myopia (grade two at baseline: ß=0.026, P<0.001; grade three at baseline: ß=0.036, P=0.001; grade four at baseline: ß=0.014, P<0.001; grade five at baseline: ß=0.039, P<0.001; grade six at baseline: ß=0.04, P<0.001). CONCLUSIONS: Refractive errors correlated significantly with academic performance among schoolchildren in China. Children with high academic performance were more likely to have faster myopia development.

Real-world big data demonstrates prevalence trends and developmental patterns of myopia in China: a retrospective, multicenter study.

BACKGROUND: Myopia is a complex disease caused by a combination of multiple pathogenic factors. Prevalence trends and developmental patterns of myopia exhibit substantial variability that cannot be clearly assessed using limited sample sizes. This study aims to determine the myopia prevalence over the past 60 years and trace the myopia development in a school-aged population using medical big data. METHODS: The refraction data from electronic medical records in eight hospitals in South China were collected from January 2005 to October 2018; including patients' year of birth, refraction status, and age at the exam. All optometry tests were performed in accordance with standard procedures by qualified senior optometrists. The cross-sectional datasets (individuals with a single examination) and longitudinal datasets (individuals with multiple examinations) were analyzed respectively. SAS statistical software was used to extract and statistically analyse all target data and to identify prevalence trends and developmental patterns related to myopia. RESULTS: In total, 1,112,054 cross-sectional individual refraction records and 774,645 longitudinal records of 273,006 individuals were collected. The myopia prevalence significantly increased among individuals who were born after the 1960s and showed a steep rise until reaching a peak of 80% at the 1980s. Regarding developmental patterns, the cross-sectional data demonstrated that the myopia prevalence increased dramatically from 23.13% to 82.83% aging from 5 to 11, and the prevalence stabilized at the age of 20. The longitudinal data confirmed the results that the age of myopic onset was 7.47±1.67 years, the age of myopia stabilized at 17.14±2.61 years, and the degree of myopia stabilized at -4.35±3.81 D. CONCLUSIONS: The medical big data used in this study demonstrated prevalence trends of myopia over the past 60 years and revealed developmental patterns in the onset, progression and stability of myopia in China.

Artificial intelligence-tutoring problem-based learning in ophthalmology clerkship.

BACKGROUND: Artificial intelligence (AI) is an increasingly popular tool in medical investigations. However, AI's potential of aiding medical teaching has not been explored. This study aimed to evaluate the effectiveness of AI-tutoring problem-based-learning (PBL) in ophthalmology clerkship and to assess the student evaluations of this module. METHODS: Thirty-eight Grade-two students in ophthalmology clerkship at Sun Yat-Sen University were randomly assigned to two groups. In Group A, students learned congenital cataracts through an AI-tutoring PBL module by exploring and operating an AI diagnosis platform. In Group B, students learned congenital cataracts through traditional lecture given with the same faculty. The improvement in student performance was evaluated by comparing the pre- and post-lecture scores of a specific designed test using paired-T tests. Student evaluations of AI-tutoring PBL were measured by a 17-item questionnaire. RESULTS: The post-lecture scores were significantly higher than the pre-lecture scores in both groups (Group A: P<0.0001, Group B: P<0.0001). The improvement of group A in the part of sign and diagnosis test (Part I) was more significant than that of group B (P=0.016). However, there was no difference in the improvement in the part of treatment plan test (Part II) between two groups (P=0.556). Overall, all respondents were satisfied and agreed that AI-tutoring PBL was helpful, effective, motive and beneficial to help develop critical and creative thinking. CONCLUSIONS: The application of AI-tutoring PBL into ophthalmology clerkship improved students' performance and satisfaction. AI-tutoring PBL teaching showed advantage in promoting students' understanding of signs of diseases. The instructors play an indispensable role in AI-tutoring PBL curriculum.

Dynamic profile of ocular refraction in pediatric cataract patients after lens surgeries.

AIM: To study the change in ocular refraction in patients with pediatric cataracts (PCs) after lens extraction. METHODS: A total of 1258 patients who were undergoing cataract extraction with/without intraocular lens (IOL) implantation were recruited during preoperative examinations between Jan 2010 and Oct 2013. Patient ages ranged from 1.5mo to 14y. Follow-ups were conducted at 1wk, 1, and 3mo postoperatively and every 3mo in the first year, then 6mo thereafter. Ocular refraction [evaluated as spherical equivalent (SE)] and yearly myopic shift (YMS) were recorded and statistically analyzed among patients with age at surgery, baseline ocular refraction, gender, postoperative time and laterality (bilateral vs unilateral). RESULTS: By Dec 31st 2015, 1172 participants had been followed for more than 2y. The median follow-up period was 3y. The critical factors affecting the ocular refraction of PC patients were baseline ocular refraction, postoperative time for both aphakic and pseudophakic eyes. YMS grew most rapidly in young childhood and early adolescence. CONCLUSION: After lens surgeries, ocular refraction in PC patients shows an individual difference of change. Further concerns should be raising to monitor the rapid myopic shift at early adolescence of these patients.

Universal artificial intelligence platform for collaborative management of cataracts.

PURPOSE: To establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage. METHODS: The training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three-step strategy: (1) capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services. RESULTS: The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%-99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3) detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be 'referred', substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern. CONCLUSIONS: The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

Diagnostic Efficacy and Therapeutic Decision-making Capacity of an Artificial Intelligence Platform for Childhood Cataracts in Eye Clinics: A Multicentre Randomized Controlled Trial.

BACKGROUND: CC-Cruiser is an artificial intelligence (AI) platform developed for diagnosing childhood cataracts and providing risk stratification and treatment recommendations. The high accuracy of CC-Cruiser was previously validated using specific datasets. The objective of this study was to compare the diagnostic efficacy and treatment decision-making capacity between CC-Cruiser and ophthalmologists in real-world clinical settings. METHODS: This multicentre randomized controlled trial was performed in five ophthalmic clinics in different areas across China. Pediatric patients (aged ≤ 14 years) without a definitive diagnosis of cataracts or history of previous eye surgery were randomized (1:1) to receive a diagnosis and treatment recommendation from either CC-Cruiser or senior consultants (with over 5 years of clinical experience in pediatric ophthalmology). The experts who provided a gold standard diagnosis, and the investigators who performed slit-lamp photography and data analysis were blinded to the group assignments. The primary outcome was the diagnostic performance for childhood cataracts with reference to cataract experts' standards. The secondary outcomes included the evaluation of disease severity and treatment determination, the time required for the diagnosis, and patient satisfaction, which was determined by the mean rating. This trial is registered with ClinicalTrials.gov (NCT03240848). FINDINGS: Between August 9, 2017 and May 25, 2018, 350 participants (700 eyes) were randomly assigned for diagnosis by CC-Cruiser (350 eyes) or senior consultants (350 eyes). The accuracies of cataract diagnosis and treatment determination were 87.4% and 70.8%, respectively, for CC-Cruiser, which were significantly lower than 99.1% and 96.7%, respectively, for senior consultants (p < 0.001, OR = 0.06 [95% CI 0.02 to 0.19]; and p < 0.001, OR = 0.08 [95% CI 0.03 to 0.25], respectively). The mean time for receiving a diagnosis from CC-Cruiser was 2.79 min, which was significantly less than 8.53 min for senior consultants (p < 0.001, mean difference 5.74 [95% CI 5.43 to 6.05]). The patients were satisfied with the overall medical service quality provided by CC-Cruiser, typically with its time-saving feature in cataract diagnosis. INTERPRETATION: CC-Cruiser exhibited less accurate performance comparing to senior consultants in diagnosing childhood cataracts and making treatment decisions. However, the medical service provided by CC-Cruiser was less time-consuming and achieved a high level of patient satisfaction. CC-Cruiser has the capacity to assist human doctors in clinical practice in its current state. FUNDING: National Key R&D Program of China (2018YFC0116500) and the Key Research Plan for the National Natural Science Foundation of China in Cultivation Project (91846109).

Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study.

BACKGROUND: Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children. METHODS AND FINDINGS: Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered. CONCLUSIONS: To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia.

Clinical characteristics of young adult cataract patients: a 10-year retrospective study of the Zhongshan Ophthalmic Center.

AIM: To investigate the characteristics of young adult cataract (YAC) patients over a 10-year period. METHODS: This observational study included YAC patients aged 18-49 years who were treated surgically for the first time at the Zhongshan Ophthalmic Center in China. YAC patients were analysed and compared with patients with childhood cataract (CC) in January 2005 to December 2014. RESULTS: During the 10-year period, 515 YAC patients and 2421 inpatients with CC were enrolled. Among the YAC patients, 76.76% (109/142) of unilateral patients had a corrected distance visual acuity (CDVA) better than 20/40 in the healthy eye, whereas only 20.38% (76/373) of bilateral patients had a CDVA better than 20/40 in the eye with better visual acuity. Compared with the CC group, the YAC group had a higher proportion of rural patients (40.40% vs 31.60%, p=0.001). Furthermore, the prevalence of other ocular abnormalities in YAC patients was higher than that in patients with CC (29.71% vs 17.47%, p<0.001). CONCLUSIONS: A large proportion coming from rural areas and a high prevalence of complicated ocular abnormalities may be the most salient characteristics of YAC patients. Strengthening the counselling and screening strategy for cataract and health education for young adults are required especially for those in rural areas.

Construction and implications of structural equation modeling network for pediatric cataract: a data mining research of rare diseases.

BACKGROUND: The majority of rare diseases are complex diseases caused by a combination of multiple morbigenous factors. However, uncovering the complex etiology and pathogenesis of rare diseases is difficult due to limited clinical resources and conventional statistical methods. This study aims to investigate the interrelationship and the effectiveness of potential factors of pediatric cataract, for the exploration of data mining strategy in the scenarios of rare diseases. METHODS: We established a pilot rare disease specialized care center to systematically record all information and the entire treatment process of pediatric cataract patients. These clinical records contain the medical history, multiple structural indices, and comprehensive functional metrics. A two-layer structural equation model network was applied, and eight potential factors were filtered and included in the final modeling. RESULTS: Four risk factors (area, density, location, and abnormal pregnancy experience) and four beneficial factors (axis length, uncorrected visual acuity, intraocular pressure, and age at diagnosis) were identified. Quantifiable results suggested that abnormal pregnancy history may be the principle risk factor among medical history for pediatric cataracts. Moreover, axis length, density, uncorrected visual acuity and age at diagnosis served as the dominant factors and should be emphasized in regular clinical practice. CONCLUSIONS: This study proposes a generalized evidence-based pattern for rare and complex disease data mining, provides new insights and clinical implications on pediatric cataract, and promotes rare-disease research and prevention to benefit patients.