ObjectivesDepression and depressive symptoms are common mental disorders that have a considerable effect on patients’ health-related quality of life and satisfaction with medical care, but the prevalence of these conditions varies substantially between published studies. The aim of this study is to conduct a systematic review and meta-analysis to provide a precise estimate of the prevalence of depression or depressive symptoms among outpatients in different clinical specialties.DesignSystematic review and meta-analysis.Data sources and eligibility criteriaThe PubMed and PsycINFO, EMBASE and Cochrane Library databases were searched to identify observational studies that contained information on the prevalence of depression and depressive symptoms in outpatients. All studies included were published before January 2016. Data characteristics were extracted independently by two investigators. The point prevalence of depression or depressive symptoms was measured using validated self-report questionnaires or structured interviews. Assessments were pooled using a random-effects model. Differences in study-level characteristics were estimated by meta-regression analysis. Heterogeneity was assessed using standard χ2 tests and the I2 statistic. The study protocol has been registered with PROSPERO under number CRD42017054738.ResultsEighty-three cross-sectional studies involving 41 344 individuals were included in this study. The overall pooled prevalence of depression or depressive symptoms was 27.0% (10 943/41 344 individuals; 95% CI 24.0% to 29.0%), with significant heterogeneity between studies (p<0.0001, τ2=0.3742, I2=96.7%). Notably, a significantly higher prevalence of depression and depressive symptoms was observed in outpatients than in the healthy controls (OR 3.16, 95% CI 2.66 to 3.76, I2=72.0%, χ2=25.33). The highest depression/depressive symptom prevalence estimates occurred in studies of outpatients from otolaryngology clinics (53.0%), followed by dermatology clinics (39.0%) and neurology clinics (35.0%). Subgroup analyses showed that the prevalence of depression and depressive symptoms in different specialties varied from 17.0% to 53.0%. The prevalence of depression and depressive symptoms was higher among outpatients in developing countries than in outpatients from developed countries. Moreover, the prevalence of depression and depressive symptoms in outpatients slightly decreased from 1996 to 2010. Regarding screening instruments, the Beck Depression Inventory led to a higher estimate of the prevalence of depression and depressive symptoms (1316/4702, 36.0%, 95% CI 29.0% to 44.0%, I2=94.8%) than the Hospital Anxiety and Depression Scale (1003/2025, 22.0%, 95% CI 12.0% to 35.0%, I2=96.6%).ConclusionOur study provides evidence that a significant proportion of outpatients experience depression or depressive symptoms, highlighting the importance of developing effective management strategies for the early identification and treatment of these conditions among outpatients in clinical practice. The substantial hete...

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 P...

ObjectiveTo characterise the contributing factors that affect medical students’ subspecialty choice and to estimate the extent of influence of individual factors on the students’ decision-making process.DesignSystematic review and meta-analysis.MethodsA systematic search of the Cochrane Library, ERIC, Web of Science, CNKI and PubMed databases was conducted for studies published between January 1977 and June 2018. Information concerning study characteristics, influential factors and the extent of their influence (EOI) was extracted independently by two trained investigators. EOI is the percentage level that describes how much each of the factors influenced students’ choice of subspecialty. The recruited medical students include students in medical school, internship, residency training and fellowship, who are about to or have just made a specialty choice. The estimates were pooled using a random-effects meta-analysis model due to the between-study heterogeneity.ResultsData were extracted from 75 studies (882 209 individuals). Overall, the factors influencing medical students’ choice of subspecialty training mainly included academic interests (75.29%), competencies (55.15%), controllable lifestyles or flexible work schedules (53.00%), patient service orientation (50.04%), medical teachers or mentors (46.93%), career opportunities (44.00%), workload or working hours (37.99%), income (34.70%), length of training (32.30%), prestige (31.17%), advice from others (28.24%) and student debt (15.33%), with significant between-study heterogeneity (p<0.0001). Subgroup analyses revealed that the EOI of academic interests was higher in developed countries than that in developing countries (79.66% [95% CI 70.73% to 86.39%] vs 60.41% [95% CI 43.44% to 75.19%]; Q=3.51, p=0.02). The EOI value of prestige was lower in developed countries than that in developing countries (23.96% [95% CI 19.20% to 29.47%] vs 47.65% [95% CI 34.41% to 61.24%]; Q=4.71, p=0.01).ConclusionsThis systematic review and meta-analysis provided a quantitative evaluation of the top 12 influencing factors associated with medical students’ choice of subspecialty. Our findings provide the basis for the development of specific, effective strategies to optimise the distribution of physicians among different departments by modifying these influencing factors.

BackgroundAlthough artificial intelligence performs promisingly in medicine, few automatic disease diagnosis platforms can clearly explain why a specific medical decision is made.ObjectiveWe aimed to devise and develop an interpretable and expandable diagnosis framework for automatically diagnosing multiple ocular diseases and providing treatment recommendations for the particular illness of a specific patient.MethodsAs the diagnosis of ocular diseases highly depends on observing medical images, we chose ophthalmic images as research material. All medical images were labeled to 4 types of diseases or normal (total 5 classes); each image was decomposed into different parts according to the anatomical knowledge and then annotated. This process yields the positions and primary information on different anatomical parts and foci observed in medical images, thereby bridging the gap between medical image and diagnostic process. Next, we applied images and the information produced during the annotation process to implement an interpretable and expandable automatic diagnostic framework with deep learning.ResultsThis diagnosis framework comprises 4 stages. The first stage identifies the type of disease (identification accuracy, 93%). The second stage localizes the anatomical parts and foci of the eye (localization accuracy: images under natural light without fluorescein sodium eye drops, 82%; images under cobalt blue light or natural light with fluorescein sodium eye drops, 90%). The third stage carefully classifies the specific condition of each anatomical part or focus with the result from the second stage (average accuracy for multiple classification problems, 79%-98%). The last stage provides treatment advice according to medical experience and artificial intelligence, which is merely involved with pterygium (accuracy, >95%). Based on this, we developed a telemedical system that can show detailed reasons for a particular diagnosis to doctors and patients to help doctors with medical decision making. This system can carefully analyze medical images and provide treatment advices according to the analysis results and consultation between a doctor and a patient.ConclusionsThe interpretable and expandable medical artificial intelligence platform was successfully built; this system can identify the disease, distinguish different anatomical parts and foci, discern the diagnostic information relevant to the diagnosis of diseases, and provide treatment suggestions. During this process, the whole diagnostic flow becomes clear and understandable to both doctors and their patients. Moreover, other diseases can be seamlessly integrated into this system without any influence on existing modules or diseases. Furthermore, this framework can assist in the clinical training of junior doctors. Owing to the rare high-grade medical resource, it is impossible that everyone receives high-quality professional diagnosis and treatment service. This framework can not only be applied in hospitals with insufficient medical resources to decrease the...

There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.