Cross-cultural validation into Portuguese of a questionnaire to assess computer vision syndrome in workers exposed to digital devices / Validação intercultural em português de um questionário para avaliar a síndrome visual do computador em trabalhadores expostos a dispositivos digitais

ABSTRACT Purpose: As digital devices are increasingly used at work, valid and reliable tools are needed to assess their effect on visual health. This study aimed to translate, cross-culturally adapt, and validate the Computer Vision Syndrome Questionnaire (CVS-Q©) into Portuguese. Methods: A 5-phase process was followed: direct translation, synthesis of translation, back-translation, consolidation by an expert committee, and pretest. To run the pretest, a cross-sectional pilot study was conducted with 26 participants who completed the prefinal Portuguese version of the CVS-Q© and were asked about difficulties, comprehensibility, and suggestions to improve the questionnaire. To evaluate the reliability and validity of the Portuguese version of the CVS-Q©, a cross-sectional validation study was performed in a different sample (280 workers). Results: In the pretest, 96.2% had no difficulty in completing it, and 84.0% valued it as clear and understandable. CVS-Q© in Portuguese (Questionário da Síndrome Visual do Computador, CVS-Q PT©) was then obtained. Validation revealed the scale's good internal consistency (Cronbach's alpha=0.793), good temporal stability (intraclass correlation coefficient=0.847; 95% CI 0.764-0.902, kappa=0.839), adequate sensitivity and specificity (78.5% and 70.7%, respectively), good discriminant capacity (area under the curve=0.832; 95% CI 0.784-0.879), and adequate convergent validity with the ocular surface disease index (Spearman correlation coefficient=0.728, p<0.001). The factor analysis provided a single factor accounting for 37.7% of the explained common variance. A worker who scored ≥7 points would have computer vision syndrome. Conclusions: CVS-Q PT© can be considered an intuitive and easy-to-understand tool with good psychometric properties to measure computer vision syndrome in Portuguese workers exposed to digital devices. This questionnaire will assist in making decisions on preventive measures, interventions, and treatment and comparing exposed populations in different Portuguese-speaking countries.

RESUMO Objetivos: À medida que a utilização de equipamentos digitais no emprego aumenta, a avaliação do seu efeito na saúde visual necessita de ferramentas válidas e robustas. Este estudo teve como objetivo traduzir, adaptar culturalmente e validar para português o Questionário da Síndrome Visual do Computador (CVS-Q©). Métodos: O procedimento foi realizado em 5 fases: tradução direta, síntese da tradução, tradução inversa, consolidação por um painel de especialistas, e pré-teste. Para fazer o pré-teste foi realizado um estudo piloto transversal aplicado a uma amostra de 26 participantes que completaram a versão pré-final da versão portuguesa do CVS-Q©, questionando por dificuldades, compreensão e sugestões de melhoria do questionário. Para avaliar a confiança e validade da versão portuguesa do CVS-Q© foi realizado um estudo transversal de validação em uma amostra diferente (280 funcionários). Resultados: No préteste, 96.2% dos participantes não apresentaram dificuldades no preenchimento do questionário, enquanto 84.0% indicaram que era claro e compreensível. Obteve-se, então, o CVS-Q© em português (Questionário da Síndrome Visual do Computador, CVS-Q PT©). A sua validação revelou uma boa consistência interna da sua escala (Cronbach's alpha=0.793), boa estabilidade tem poral (coeficiente de correlação interclasse=0.847; 95% CI 0.764-0.902, kappa=0.839), sensibilidades e especificidades adequadas (78.5% e 70.7%, respetivamente), boa capacidade de discriminação (área abaixo da curva=0.832; 95% CI 0.784-0.879), e uma adequada validade da convergência com o índice de doença da superfície ocular (ocular surface disease index - OSDI; coeficiente de correlação de Spearman=0.728, p<0.001). A análise fatorial revelou um único fator responsável por explicar a variância comum em 37.7%. Um funcionário com uma pontuação ≥7 pontos sofria de síndrome visual do computador. Conclusão: O CVS-Q PT© pode ser considerada uma ferramenta intuitiva, de fácil interpretação e com boas pro priedades psicométricas para avaliar a síndrome visual do computador em funcionários portugueses expostos a ecrãs digitais. Este questionário facilitará as decisões sobre medidas preventivas, intervenções e tratamento, e a comparação entre as populações expostas em diferentes países de língua portuguesa.

Accuracy of an artificial intelligence-based mobile application for detecting cataracts: Results from a field study

Purpose: To assess the accuracy of e?Paarvai, an artificial intelligence?based smartphone application (app) that detects and grades cataracts using images taken with a smartphone by comparing with slit lamp?based diagnoses by trained ophthalmologists. Methods: In this prospective diagnostic study conducted between January and April 2022 at a large tertiary?care eye hospital in South India, two screeners were trained to use the app. Patients aged >40 years and with a best?corrected visual acuity <20/40 were recruited for the study. The app is intended to determine whether the eye has immature cataract, mature cataract, posterior chamber intra?ocular lens, or no cataract. The diagnosis of the app was compared with that of trained ophthalmologists based on slit?lamp examinations, the gold standard, and a receiver operating characteristic (ROC) curve was estimated. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed. Results: The two screeners used the app to screen 2,619 eyes of 1,407 patients. In detecting cataracts, the app showed high sensitivity (96%) but low specificity (25%), an overall accuracy of 88%, a PPV of 92.3%, and an NPV of 57.8%. In terms of cataract grading, the accuracy of the app was high in detecting immature cataracts (1,875 eyes, 94.2%), but its accuracy was poor in detecting mature cataracts (73 eyes, 22%), posterior chamber intra?ocular lenses (55 eyes, 29.3%), and clear lenses (2 eyes, 2%). We found that the area under the curve in predicting ophthalmologists’ cataract diagnosis could potentially be improved beyond the app’s diagnosis based on using images only by incorporating information about patient sex and age (P < 0.0001) and best?corrected visual acuity (P < 0.0001). Conclusions: Although there is room for improvement, e?Paarvai app is a promising approach for diagnosing cataracts in difficult?to?reach populations. Integrating this with existing outreach programs can enhance the case detection rate.

The 20/20/20 rule: Practicing pattern and associations with asthenopic symptoms

Purpose: The present work style and lifestyle have increased the digital device use. Therefore, an increase in digital eyestrain is to be expected. We undertook a survey during coronavirus disease 2019 (COVID?19) pandemic to investigate the practice of 20/20/20 rule and its association with digital device use and asthenopic symptoms. While this rule is commonly advised, little is known about its validity. Methods: An online survey form was disseminated through social media and emails. The questions for eye?related symptoms were similar to the convergence insufficiency symptom survey (CISS). Participants with age ?5 years were included, with parents completing the survey for children (?16 years). Results: A total of 432 participants (mean ± standard deviation [SD]: 26.06 ± 13.92 years) were enrolled, of which 125 responses were for children. The 20/20/20 rule was practiced only by 34% of the participants either regularly (n = 38) or occasionally (n = 109). Those who had complaints of burning sensation and headache tended to practice this rule. Among adult participants, more females (47%) practiced this rule when compared to males (23%). Also, adult females significantly (P = 0.04) had more symptoms score when compared to males. In children, no such gender difference was found. Conclusion: Only one?third of participants practice the 20/20/20 rule at least occasionally. More number of adult females being symptomatic and practicing in greater number could be due to higher prevalence of dry eye condition in females. While the symptom of burning sensation could be related to dry eye, that of headache could be related to refractive error or binocular vision dysfunctions

Status quo and influencing factors of computer vision syndrome among college freshmen in Tianjin / 中国学校卫生

Objective@#To investigate the current situation and associated factors of computer vision syndrome (CVS) among college freshmen in Tianjin during the COVID-19 epidemic, and to provide a reference for visual comfort of college students.@*Methods@#A total of 868 college freshmen from one university in Tianjin were administered with CVS qualitative analysis questionnaire, eye health status questionnaire and eye health examination during Oct to Dec 2021. Chi square test and multivariate Logistic regression were used for data analysis.@*Results@#The detection rate of CVS among the included students was 68.5% ( n =595) and was higher in females (72.2%) than in males (61.7%). The CVS detection rate in girls, students without myopia, >30 min sleep onset, >1 h mobile phone usage, and ≤8 h sleep duration (72.2%, 70.4%, 81.1%, 72.7%, 71.2%) were significantly higher than boys, students with low grade myopia, sleep onset required ≤30 min, use mobile phone for ≤1 h, and sleep duration >8 h(61.7%, 63.3 %, 67.4%, 65.9%, 61.1%) ( χ 2=10.08, 3.94, 5.89, 4.40, 7.94, P ＜0.05). Differences in CVS detection rates varied significantly by daily electronic device usage and academic stress students ( χ 2=22.03, 21.24, P ＜0.05). Multivariate Logistic regression analysis showed that daily use of electronic devices 4-6, 7-9, ≥10 h, sleep onset required >30 min, moderate to higher academic pressure were positively associated with CVS ( OR=1.95, 2.94, 2.30, 2.39, 3.51, 4.41, P ＜0.05), boys, low grade myopia, night sleep time >8 h were negatively associated with CVS ( OR=0.65, 0.70, 0.65, P ＜0.05).@*Conclusion@#The detection rate of CVS among freshmen in a university in Tianjing is high. Attention should be paid to the CVS situation of students with e learning, and general public should also be educated to reduce the time of unnecessary electronic product use and ensure night sleep to reduce the prevalence of CVS.

Application prospect of computer vision technology assisted ultrasonography in diagnosis and treatment of liver diseases / 中华消化外科杂志

Ultrasound examination has the advantages of non-radiation, non-invasive, low cost and high efficiency, and is the most commonly used method of liver imaging examination. In recent years, the application of computer vision technology to the intelligent analysis of ultrasound images has become a research hotspot in the field of intelligent healthcare. Through large-scale data training, the intelligent analysis model of ultrasound omics based on machine learning algorithm can assist clinical diagnosis and therapy, and improve the efficiency and accuracy of diagnosis. Based on the literature, the authors summarize the application proprect of computer vision technology assisted ultrasonography in the evaluation of diffuse liver lesions, focal liver lesions, microvascular invasion of liver cancer, postoperative recurrence of liver cancer, and postoperative therapy response to trans-catheter arterial chemoembolization.

Computer Vision in Medical Imaging and its Impact on the Rare Musculoskeletal Diseases / 罕见病研究

There are over 6000 rare diseases in the world, affecting more than 300 million people. Early and precise diagnosis of rare diseases has always been the goal in clinical medicine. Emerging computer vision technology now greatly enhance medicine and healthcare and shows the potential in assisting the diagnosis and treatment for rare diseases. The technology can be a useful tool for extracting disease-relevant patterns from medical imaging. However, the effectiveness of its application depends on the complexity of the medical cases. In this paper, we summarize the challenges and emerging solution for the application of computer vision in diagnosis, rehabilitation as well as management of rare musculoskeletal diseases.

Predicción del Síndrome Informático Visual mediante la adicción a videojuegos en estudiantes chinos y españoles / Prediction of computer vision syndrome by video game addiction in Chinese and Spanish students

Introducción: El uso de los videojuegos, por la extensión que ha llegado a alcanzar durante la pandemia de la COVID-19, es una variable relevante de estudio, especialmente por sus interacciones con aspectos de la salud mental y visual. Objetivo: predecir el nivel de Síndrome informático visual a partir de un índice optimizado sobre el nivel de adicción en estudiantes universitarios de dos poblaciones: española y china. Material y Métodos: Se administró un cuestionario online con tres instrumentos validados: un cuestionario para evaluar el juego con videojuegos (CHCVI), un cuestionario para evaluar la adicción a los videojuegos (CERV) y un cuestionario para detectar el síndrome visual por ordenador (CSQ). Los tres cuestionarios se aplicaron a una muestra de 253 estudiantes, tanto de universidades chinas como españolas. Para establecer las predicciones, se construyeron índices robustos basados en el análisis factorial de los instrumentos administrados. Finalmente, se aplicó una regresión logística para obtener un modelo matemático útil para predecir el Síndrome Informático Visual. Resultados: Los resultados mostraron un mayor síndrome informático visual y apetito por los videojuegos en los estudiantes españoles, y menores puntuaciones de síndrome informático visual pero una mayor alteración de la vida cotidiana en los estudiantes chinos debido a este tipo de ocio. Además, se comprobó que los estudiantes de la muestra china tenían un menor riesgo de padecer el síndrome informático visual, y que tener mayores niveles de adicción implicaba 1,4 veces más probabilidades de sufrir dicho síndrome. Conclusiones: Los presentes hallazgos demuestran una relación hasta ahora inexplorada entre la adicción a los videojuegos y los síntomas visuales relacionados con el abuso del ocio electrónico.

Introduction: The use of video games, due to the extent that it has reached during the COVID-19 pandemic, is a relevant study variable especially because of its interactions with aspects of mental and visual health. Objective: to predict the occurrence of computer vision syndrome according to the level of addiction to video games in university undergraduates during a particular period of uncertainty due to health and mobility restrictions imposed by governments as a result of the COVID-19 pandemic. Material and Methods: To accomplish this objective, an online questionnaire was administered with three validated instruments: a questionnaire to assess playing video games (CHCVI), a questionnaire to evaluate video games addiction (CERV), and a questionnaire to detect computer vision syndrome (CSQ). The three questionnaires were applied to a sample of 253 students from both Chinese and Spanish universities. To establish the predictions, robust indexes were constructed based on the Factor Analysis of the instruments administered. Finally, logistic regression was applied to predict computer vision syndrome. Results: The results showed greater computer vision syndrome and appetite for video games in Spanish students, and lower computer vision syndrome scores but a greater alteration of daily life in chinese students due to this type of leisure. Moreover, it was found that students from the Chinese sample entailed a lower risk of suffering from computer vision syndrome, and that having higher levels of addiction involved 1,4 times more likelihood of suffering from such syndrome. Conclusions: The present findings demonstrate a previously unexplored relationship between video games addiction and visual symptoms related to screen exposure.

Target tracking in complex scenes based on computer vision / Rastreamento de alvos em cenas complexas com base na visão computadorizada / Seguimiento de blancos en escenas complejas con base en la visión computadorizada

ABSTRACT Objective: Use the deep learning network model to identify key content in videos. Methodology: After reviewing the literature on computer vision, the feature extraction of the target video from the network using deep learning with the time-series data enhancement method was performed. The preprocessing method for data augmentation and Spatio-temporal feature extraction on the video based on LI3D network was explained. Accuracy rate, precision, and recall were used as indices. Results: The three indicators increased from 0.85, 0.88, and 0.84 to 0.89, 0.90, and 0.88, respectively. This shows that the LI3D network model maintains a high recall rate accompanied by high accuracy after data augmentation. The accuracy and loss function curves of the training phase show that the accuracy of the network is greatly improved compared to I3D. Conclusion: The experiment proves that the LI3D model is more stable and has faster convergence. By comparing the accuracy curve and loss function curve during LI3D, LI3D-LSTM, and LI3D-BiLSTM training, it is found that the LI3D-BiLSTM model converges faster. Level of evidence II; Therapeutic studies - investigation of treatment results.

RESUMO Objetivo: Usar o modelo de rede de aprendizagem profunda para identificar o conteúdo-chave em vídeos. Metodologia: Após revisão da literatura sobre a visão computadorizada, efetuou-se a extração da característica do vídeo alvo da rede utilizando o aprendizado profundo com o método de melhoramento de dados em séries temporais. Foi explanado o método de pré-processamento para aumento de dados e extração da característica espaço-temporal no vídeo baseado na rede LI3D. Foram utilizados como índices a taxa de precisão, precisão e recall. Resultados: Os três indicadores aumentaram de 0,85, 0,88, e 0,84 para 0,89, 0,90, e 0,88, respectivamente. Isso mostra que após o aumento dos dados, o modelo de rede LI3D mantém uma alta taxa de recuperação acompanhada de uma alta precisão. As curvas de precisão e função de perda da fase de treinamento demonstram que a precisão da rede é muito melhorada em comparação com a I3D. Conclusão: O experimento prova que o modelo LI3D é mais estável e que a convergência é mais rápida. Ao comparar a curva de precisão e a curva de função de perda durante o treinamento LI3D, LI3D-LSTM e LI3D-BiLSTM, verifica-se que o modelo LI3D-BiLSTM converge mais rapidamente. Nível de evidência II; Estudos terapêuticos - investigação de resultados de tratamento.

RESUMEN Objetivo: Utilizar el modelo de red de aprendizaje profundo para identificar el contenido clave en los vídeos. Metodología: Después de revisar la literatura sobre visión por ordenador, se realizó la extracción de características del vídeo objetivo de la red utilizando el aprendizaje profundo con el método de aumento de datos de series temporales. Se explicó el método de preprocesamiento para el aumento de datos y la extracción de características espacio-temporales en el vídeo basado en la red LI3D. Se utilizaron como índices la tasa de exactitud, la precisión y recall. Resultados: Los tres indicadores aumentaron de 0,85, 0,88 y 0,84 a 0,89, 0,90 y 0,88, respectivamente. Esto demuestra que el modelo de red LI3D mantiene un alto índice de recuperación acompañado de una alta precisión tras el aumento de datos. Las curvas de precisión y de función de pérdida de la fase de entrenamiento muestran que la precisión de la red mejora mucho en comparación con la I3D. Conclusión: El experimento demuestra que el modelo LI3D es más estable y tiene una convergencia más rápida. Al comparar la curva de precisión y la curva de función de pérdida durante el entrenamiento de LI3D, LI3D-LSTM y LI3D-BiLSTM, se observa que el modelo LI3D-BiLSTM converge más rápidamente. Nivel de evidencia II; Estudios terapéuticos - investigación de resultados de tratamiento.

Digital eye strain and its associated factors in children during the COVID-19 pandemic

Purpose: This study was undertaken to identify the prevalence of symptoms related to the use of display devices and contributing factors in children engaged in distance learning during the COVID?19 pandemic. Methods: An online electronic survey form was prepared using Google Forms (Alphabet Co., Mountain View, CA) and sent to parents of children under the age of 18 years engaged in distance learning during the COVID?19 pandemic. The types of display devices children use, how often such devices are used, the symptoms of digital eye strain, and the severity and frequency of the symptoms were recorded, and the associations between the factors were analyzed. Results: A total of 692 participants were included. The mean age of the children was 9.72 ± 3.02 years. The most common display devices used were personal computers (n = 435, 61.7%) for online classes and smartphones (n = 400, 57.8%) for nonacademic purposes. The mean duration of display device use was 71.1 ± 36.02 min without a break and 7.02 ± 4.55 h per day. The most common reported symptom was headache (n = 361, 52.2%). Of the participants, 48.2% (n = 332) reported experiencing 3 or more symptoms. The multivariate analysis detected that being male (P = 0.005) and older age (P = 0.001) were independent risk factors for experiencing 3 or more symptoms. Conclusion: The increasing use of digital devices by children is exacerbating the problem of digital eye strain in children as a side effect of online learning. Public awareness should be improved

Association of screen time, quality of sleep and dry eye in college-going women of Northern India

Purpose: To evaluate the association of daily screen time and quality of sleep with the prevalence of dry eye among college?going women. Methods: This study was a cross?sectional, comparative questionnaire?based study of 547 college?going women in northern India. A 10?item Mini Sleep Questionnaire was used to check the quality of sleep, and the Standard Patient Evaluation of Eye Dryness (SPEED) scale was used to examine the prevalence of dry eye among college?going women. Results: Multinomial logistic regression showed a significant association between dry eye with daily screen time spent (P < 0.05) and the quality of sleep (P < 0.05) among college?going girls. Using Latent Class Analysis, two latent classes were selected based on the Bayesian Information Criteria. It was found that the majority population falls in class two and was having Severe Sleep?Wake difficulty. It was seen that the participants in class two belonged to the age bracket of 18–21 years, were from stream Humanities, education of father and mother equal to graduation, father working only, belonging to the nuclear family, having one sibling, hailing from the urban locality, spending more than 6 h daily on?screen, a majority of them using mobile phones, not using eye lubricants, and reported an increase in screen time during COVID?19. Conclusion: Dry eye and sleep quality are essential global health issues, and coupled with increased screen time, may pose a challenge in the present era. Preventive strategies need to be incorporated in school and college curriculums to promote physical, social, and psychological well?being and quality of life

Application of artificial intelligence in colorectal surgery / 国际外科学杂志

With the dramatically development of artificial intelligence (AI), especially the advent of deep learning, now it can be applied to medicine reliably and efficiently. In the field of colorectal surgery, the application of AI has resulted in profound affect. The detection of colon polyp and assessment of invasiveness depth of colorectal cancer were improved by AI-assisted colonoscopy. Based on the routine data from medical imaging, demographic and clinicopathological parameters, AI may provide more accurate predictions about prognosis, surgical complication and outcome, so to help decision making and perioperative management. And the advent of real intelligent operative robot will make automatic operation possible in the future. The application of AI will improve the development of colorectal surgery significantly and make it more precise, effective and intelligent.

Establishment of a deep learning-based visual model for intelligent recognition of Oncomelania hupensis / 中国血吸虫病防治杂志

Objective To establish a deep learning-based visual model for intelligent recognition of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, and evaluate the effects of different training strategies for O. hupensis image recognition. Methods A total of 2 614 datasets of O. hupensis snails and 4 similar snails were generated through field sampling and internet capture, and were divided into training sets and test sets. An intelligent recognition model was created based on deep learning, and was trained and tested. The precision, sensitivity, specificity, accuracy, F1 score and Youden index were calculated. In addition, the receiver operating characteristic (ROC) curve of the model for snail recognition was plotted to evaluate the effects of “new learning”, “transfer learning” and “transfer learning + data enhancement” training strategies on the accuracy of the model for snail recognition. Results Under the “transfer learning + data enhancement” strategy, the precision, sensitivity, specificity, accuracy, Youden index and F1 score of the model were 90.10%, 91.00%, 97.50%, 96.20%, 88.50% and 90.51% for snail recognition, which were all higher than those under both “new learning” and “transfer learning” strategies. There were significant differences in the sensitivity, specificity and accuracy of the model for snail recognition under “new learning”, “transfer learning” and “transfer learning + data enhancement” training strategies (all P values < 0.001). In addition, the area under the ROC curve of the model was highest (0.94) under the “transfer learning + dataenhancement” training strategy. Conclusions This is the first visual model for intelligent recognition of O. hupensis based on deep learning, which shows a high accuracy for snail image recognition. The “transfer learning + data enhancement” training strategy is helpful to improve the accuracy of the model for snail recognition.

Robotic arm control system based on augmented reality brain-computer interface and computer vision / 生物医学工程学杂志

Brain-computer interface (BCI) has great potential to replace lost upper limb function. Thus, there has been great interest in the development of BCI-controlled robotic arm. However, few studies have attempted to use noninvasive electroencephalography (EEG)-based BCI to achieve high-level control of a robotic arm. In this paper, a high-level control architecture combining augmented reality (AR) BCI and computer vision was designed to control a robotic arm for performing a pick and place task. A steady-state visual evoked potential (SSVEP)-based BCI paradigm was adopted to realize the BCI system. Microsoft's HoloLens was used to build an AR environment and served as the visual stimulator for eliciting SSVEPs. The proposed AR-BCI was used to select the objects that need to be operated by the robotic arm. The computer vision was responsible for providing the location, color and shape information of the objects. According to the outputs of the AR-BCI and computer vision, the robotic arm could autonomously pick the object and place it to specific location. Online results of 11 healthy subjects showed that the average classification accuracy of the proposed system was 91.41%. These results verified the feasibility of combing AR, BCI and computer vision to control a robotic arm, and are expected to provide new ideas for innovative robotic arm control approaches.

A preliminary study of sperm identification in microdissection testicular sperm extraction samples with deep convolutional neural networks / 亚洲男科学杂志(英文版)

Sperm identification and selection is an essential task when processing human testicular samples for in vitro fertilization. Locating and identifying sperm cell(s) in human testicular biopsy samples is labor intensive and time consuming. We developed a new computer-aided sperm analysis (CASA) system, which utilizes deep learning for near human-level performance on testicular sperm extraction (TESE), trained on a custom dataset. The system automates the identification of sperm in testicular biopsy samples. A dataset of 702 de-identified images from testicular biopsy samples of 30 patients was collected. Each image was normalized and passed through glare filters and diffraction correction. The data were split 80%, 10%, and 10% into training, validation, and test sets, respectively. Then, a deep object detection network, composed of a feature extraction network and object detection network, was trained on this dataset. The model was benchmarked against embryologists' performance on the detection task. Our deep learning CASA system achieved a mean average precision (mAP) of 0.741, with an average recall (AR) of 0.376 on our dataset. Our proposed method can work in real time; its speed is effectively limited only by the imaging speed of the microscope. Our results indicate that deep learning-based technologies can improve the efficiency of finding sperm in testicular biopsy samples.

Evaluation of the effects of single vision lenses with additional near-power on computer-induced asthenopia / Avaliação dos efeitos de lentes de visão simples com poder adicional de perto na astenopia induzida por computador

Abstract Objective: Compared to standard spectacle lenses, do +0.40 EyeZenTM lenses reduce symptoms of asthenopia induced by computer? Methods: A prospective clinical study was carried out with 39 volunteers who spent more than 4 hours a day using a computer (age, 27.31±4.24; male: female =13:26). Asthenopia and visual comfort were assessed using a questionnaires. All participants completed the asthenopia questionnaire with updated regular lenses (baseline). After 4 weeks of +0.40 Eyezen™ lenses wearing all subjects answered the asthenopia questionnaire and a second questionnaire to establish their level of satisfaction with these lenses. Statistical analysis was performed usind the Shapiro-Wilk test and Wilcoxon test, and p-values less than 0.05 were considered statistically significant. Results: Compared to standard spectacle lenses (baseline), +0.40 EyeZenTM lenses wearing reduced the total asthenopia score from17.44 ± 5.51 to 13.18 ± 10.22 (p < 0.001). Regarding the perception of the visual comfort levels with these lenses in the management of digital devices, more than 90% of subjects said they were entirely or delighted with their visual comfort.. Conclusions: Digital asthenopia induced by computer was significantly reduced by +0.40 EyeZen lenses wearing.

Resumo Objetivo: Comparadas com lentes oftálmicas regulares, as lentes de visão simples com +0,40D de poder adicicional de perto reduzem os sintomas de astenopia induzida por computador? Métodos: Foi realizado um estudo clínico prospectivo com 39 voluntários que passavam mais de 4h diárias utilizando computador (idade: 27,31±4,24 anos; masculino:feminino = 26:13). A astenopia e a percepção do conforto visual foram avaliadas com questionários. Todos os participantes respoderam ao questionário de astenopia com lentes regulares atualizadas (baseline). Após 4 semanas de uso das lentes +0.40 Eyezen™ os participantes responderam aos questionários de astenopia e de conforto visual. A análise estatística foi feita com os testes de Shapiro-Wilk e Wilcoxon. Valores de p<0,05 foram considerados estatísticamente significantes. Resultados: Comparadas com lentes oftálmicas regulares (baseline), o uso das lentes de visão simples com +0,40D de poder adicional de perto reduziu o escore total de astenopia de 17,44 ± 5,51 para 13,18± 10,22 (p< 0,001). Mais de 90% dos participantes se declaram completamente ou muito satisfeitos com o conforto visual percebido no uso de dispositivos digitais. Conclusão: A astenopia induzida por computadores foi significativamente reduzida pelo uso das lentes +0,40 Eyezen™ combinadas Crizal® Sapphire™.

Assessment of Computer Vision Syndrome in Software Professionals

Background: According to National Institute of Occupational Safety and Health, computer vision syndrome affects 90% people who spend more than 3 hours a day on the computer. It is a group of eye and vision related problems. It is a temporary condition resulting from focusing the eyes on a computer display for prolonged, uninterrupted period of time. Also different reactions of the eye and the brain to the characters on the screen cause strain. Hence, this study is planned to assess computer vision syndrome in software professionals. Objectives: To assess Computer Vision Syndrome by history and clinical examination. Material and Methods: An assessment survey was conducted in a convenient software company. A total number of 60 people in age group 28-40 years having minimum exposure of three years to computer everyday were recruited in the study. A questionnaire was developed to collect data about perceived symptoms on computer vision syndrome. Results: It was observed that 80% of subjects suffer from backache, wrist and shoulder pain. 72% subjects complained of eyestrain and 70% complained of dry and irritated eyes. 62% subjects complained of headache. More than 50% subjects gave history of watering and redness of eyes. Conclusion: Study shows that more than 50% subjects suffer from some or the other symptom of computer vision syndrome. Early detection and prevention of computer vision syndrome is necessary to prevent future complications and better health of software professionals.

Curse of the technology-computer related musculoskeletal disorders and vision syndrome: a study

Background: Computer Related Musculoskeletal disorders and Vision Syndrome (CRMSKVS) is defined as symptoms due to prolonged use of Visual Display Terminal (VDT).Methods: A cross-sectional observational study was done among office-workers working on computer terminal. A self-reported questionnaire was distributed and Musculoskeletal (MSK) and visual symptoms in the preceding 12 months (01 October 2017 to 30 September 2018) were taken as dependent variable. Multivariate analysis was done to identify the determinants of CRMSKVS.Results: Responses from 1193 subjects were included in the study. CRMSKVS was present in 489 cases (40.98%; males - 37.5%, females - 58.29%).  The main MSK symptoms were pain/stiffness in neck (40.98%), shoulder (38.99%), lower back (35.6%) and elbow/wrist/hand/fingers (23.1%). The ocular symptoms were excessive watering (39.6%), pain (24.99), irritation (18.6%), burning/itching sensation (29.8%), redness (40.7%), blurring of vision (13.2%) and headache (40.9%). Female gender (OR-1.498(1.262-1.778)), long duration of working hours (OR-2.77(2.399-3.214)), poor break duration (OR-2.59(2.172-3.089)), excessive smart phone use (OR-2.071(1.834-2.338)), poor posture (OR-3.883(3.282-4.592)), inappropriate distance of computer screen (OR-2.173(1.829-2.582)), low height of screen (OR-1.936(1.527-2.454)), distance of keyboard (OR-3.161(2.528-3.953)) and distance of mouse (OR-5.785(3.932-8.512)) were identified as significant determinants of CRMSKVS.Conclusions: CRMSKVS is an emerging pandemic which needs urgent attention by medical and administrative authorities. The device factors, personal factors, environmental and ergonomic factors are the modifiable risk factors for CRMSKVS.

Current applications of artificial intelligence for intraoperative decision support in surgery / 医学前沿

Research into medical artificial intelligence (AI) has made significant advances in recent years, including surgical applications. This scoping review investigated AI-based decision support systems targeted at the intraoperative phase of surgery and found a wide range of technological approaches applied across several surgical specialties. Within the twenty-one (n = 21) included papers, three main categories of motivations were identified for developing such technologies: (1) augmenting the information available to surgeons, (2) accelerating intraoperative pathology, and (3) recommending surgical steps. While many of the proposals hold promise for improving patient outcomes, important methodological shortcomings were observed in most of the reviewed papers that made it difficult to assess the clinical significance of the reported performance statistics. Despite limitations, the current state of this field suggests that a number of opportunities exist for future researchers and clinicians to work on AI for surgical decision support with exciting implications for improving surgical care.

A cross sectional overview of digital eye strain: a growing health concern in this digital age in central India (Madhya Pradesh)

Background: The physical discomfort and collection of symptoms after digital screen use for longer than two hours at a time is referred by the Vision Council as digital eye strain (DES). Common symptoms of DES are eyestrain, headache, blurred vision, dry eyes and pain in neck and shoulders. This study aims to know about the prevalence; factors associated with and awareness about preventive measures for DES among college students.Methods: A cross-sectional study was conducted for 2 months in 2017 among randomly selected 200 college students of 20 to 30 years of age in Indore city of Madhya Pradesh using a pre-designed semi-structured questionnaire.Results: Mean age of participants was 22.5 years; of which 58% were females. Of the respondents, 89.5% (179 students) reported experiencing DES. Average distance from digital screen, brightness level of digital device, use of digital device before going to sleep and awareness about appropriate distance of digital screen from eyes had statistically significant association with having digital eye strain. 98% of respondents were unaware of the term DES while >60% did not have knowledge about the harmful blue light emitted by digital devices, protective use of digital screen filters, appropriate distance of viewing digital screen and the 20-20-20 rule of taking breaks in between screen time.Conclusions: Since digital device use is a necessary evil; better ergonomic practices to avoid DES should be adopted. Opportunistic health promotion and patient education undertaken by ophthalmologists on an OPD basis is one solution.

Evaluating the diameter of eyedropper tips using a computer vision system / Avaliação do diâmetro de bicos de conta-gotas de colírios por visão computacional

ABSTRACT Purpose: This study aimed to determine the variation in diameters of outer and inner apertures of eyedropper tips using a computer vision system. Standardizing the size of eye drop nozzles is crucial to reduce the treatment cost of chronic eye diseases and to ensure a continued use of medication. An eyedropper volume of >20 µL (maximum storage of the conjunctival sac) causes medication wastage and increases treatment costs. Methods: We measured the diameters of the outer and inner apertures of eyedropper tips and evaluated variations in diameters using a computerized visual inspection system. Results: The computer visual inspection system identified anomalies in the apertures of eyedropper tips that resulted in diameter variations. Conclusions: The results of the present study show discrepancies in diameters of eyedropper tips, suggesting a variation in eyedropper size and medication wastage.

RESUMO Objetivo: Este estudo teve como objetivo determinar a variação dos diâmetros das aberturas externa e interna dos bicos conta-gotas utilizando sistema de visão computacional. A padronização do tamanho dos colírios conta-gotas é importante para reduzir o custo do tratamento de doenças crônicas e garantir o uso contínuo de medicamentos. O volume da gota maior do que 20 µl (volume de armazenamento máximo do saco conjuntival) gera desperdício da medicação e aumenta o custo do tratamento. Métodos: Medimos os diâmetros das aberturas externa e interna das pontas dos conta-gotas e avaliamos as variações no diâmetro usando um sistema de inspeção visual computadorizado. Resultados: O sistema de inspeção visual por computador identificou anomalias nas aberturas dos bicos dos frascos conta-gotas que resultaram em variações de diâmetro. Conclusões: Os resultados do presente estudo mostram discrepâncias nos diâmetros dos bicos dos frascos dos conta-gotas, sugerindo uma variação no tamanho das gotas e no desperdício de remédios.