The role of the occupational physician in the early detection of occupational diseases.

The preventive medicine foundation of occupational medicine impacts all the different practice areas of OEM, whether it involves preventing illness related to a hazardous exposure, treating injury due to an employee health condition, or addressing unnecessary work disability after an occupational injury. The OEM physicians are required to effectivelyinteractwithawiderangeofother professionals and provide guidance not only to patients but also to other clinicians and occupational health nurses, employers, safety and industrial hygiene professionals, human resource managers, attorneys, labor unions, and public health care professionals. In these interactions,OEMphysiciansshouldfollowa strict code of ethics related to matters of confidentiality and potential conflicts of interest. Detailed guidance in this area is provided in ACOEM Code of Ethics. 16 Effective communication in OEM includes risk communication, patient education, workforce education, development of policy and guidance documents, and medical/legal document preparation. Physicians practicing OEM typically

To highlight the role of occupational physician (OP) in occupational injuries (OI) prevention and management. To suggest an approach beyond traditional focus on descriptive epidemiology, engineering interventions, administrative aspects of OI prevention. To promote a person- and enterprise-tailored approach, entailing greater attention to human factors and to practical problems of the specific workplace, with a call to a leading role played by OP. Analysis of the literature on the broader topic of OI prevention revealed thousands of publications; however, only a handful of them mention or describe the participation of OP in OI prevention. While recognizing that literature search is not the proper and only way to appreciate the current role of OP in this field, therefore, it seems necessary to call OP to a stronger effort in prevention and management of OI, through the context of a comprehensive intervention in cooperation with managers, supervisors, safety personnel and workers, focusing on specific needs of each enterprise. The following areas of OP intervention were examined: risk assessment, health surveillance, management, scientific research and health education. Within each of these topics, possible contributions, methodologies, instruments available for the OP were discussed, taking into account the relevant literature. Pathways for practical applications were illustrated, e.g., OI data generation and analyses, predictors of OI, fitness for work, case management, team work, educational issues, first aid, suggestion for OP contribution in specific research questions. OI continue to take a remarkable toll from individuals and society. New multidisciplinary interventions are needed to prevent OI. Focused activities at the single worksite with a central role from OP are definite options. OP is an effective interface between workforce and management and may offer, through a proactive approach, valuable practical and cultural contributions, while respecting technical and ethical guidelines of occupational health professionals.

In Tanzania, smallholder farmers are mainly involved in farming activities which contribute significantly to food security and nutrition. Kagera, Mbeya and Arusha regions lead in high banana production, however, diseases and pests are threats to the yields. Early detection and identification of banana diseases is still a challenge for smallholder farmers and extension officers due to lack of the necessary tools such as sensors and mobile applications. In this research, an early detection tool for banana fungal diseases was developed. This research presents deep learning models trained for the detection of banana diseases of Fusarium wilt and Black sigatoka and deployed on a smartphone for the early detection of the diseases in real time. The five models selected and trained include VGG16, Resnet18, Resnet50, Resnet152 and InceptionV3. The VGG16 model achieved an accuracy of 97.26%, Resnet50 achieved an accuracy of 98.8%, Resnet18 achieved an accuracy of 98.4%, InceptionV3 achieved an accuracy of 95.41% and Resnet152 achieved an accuracy of 99.2%. We therefore, used InceptionV3 model for deployment in mobile phones because it has low computation cost and low memory requirements of the all models. The developed tool was capable of detecting diseases with 99% of confidence of the captured leaves from the real world environment. The system was developed on Android based application in English language. The developed tool has the potential to support smallholder farmers and extension officers to detect banana fungal disease at early stages. We conclude that early detection of the diseases is important. Hence control and management of banana fungal diseases will be done early for the improvement of banana yield.

Onion crops are affected by many diseases at different stages of growth, resulting in significant yield loss. The early detection of diseases helps in the timely incorporation of management practices, thereby reducing yield losses. However, the manual identification of plant diseases requires considerable effort and is prone to mistakes. Thus, adopting cutting-edge technologies such as machine learning (ML) and deep learning (DL) can help overcome these difficulties by enabling the early detection of plant diseases. This study presents a cross layer integration of YOLOv8 architecture for detection of onion leaf diseases viz.anthracnose, Stemphylium blight, purple blotch (PB), and Twister disease. The experimental results demonstrate that customized YOLOv8 model YOLO-ODD integrated with CABM and DTAH attentions outperform YOLOv5 and YOLO v8 base models in most disease categories, particularly in detecting Anthracnose, Purple Blotch, and Twister disease. Proposed YOLOv8 model achieved the highest overall 77.30% accuracy, 81.50% precession and Recall of 72.10% and thus YOLOv8-based deep learning approach will detect and classify major onion foliar diseases while optimizing for accuracy, real-time application, and adaptability in diverse field conditions.

This paper presents a study on the image processing techniques used to identify and classify fungal disease symptoms affected on different agriculture/horticulture crops. Many diseases exhibit general symptoms that are be caused by different pathogens produced by leaves, roots etc. Images Often do not possess sufficient details to assist in diagnosis, resulting in waste of time, misshaping the diagnostician to arrive at incorrect diagnosis. Farmers experience great difficulties and also in changing from one disease control policy to another i.e. intensive use of pesticides. Farmers are also concerned about the huge costs involved in these activities and severe loss. The cost intensity, automatic correct identification and classification of diseases based on their particular symptoms is very useful to farmers and also agriculture scientists. Early detection of diseases is a major challenge in horticulture / agriculture science. Development of proper methodology, certainly of use in these areas. Plant diseases are caused by bacteria, fungi, virus, nematodes, etc., of which fungi is the main disease causing organism. The present study has been focused on early detection and classification of fungal disease and its related symptoms.

Simple SummaryThis paper provides a review of recent studies exploring the application of artificial intelligence (AI) in the early detection and monitoring of respiratory disease in swine, emphasizing the significance of early detection for preventing economic losses. The studies primarily focus on utilizing coughing sounds as a feature in disease recognition, comparing different AI models and methodologies. A commercially available AI system that integrates temperature and humidity sensors with audio technologies for respiratory health monitoring through cough-sound identification is also assessed. However, the limitations of the current technology are identified, highlighting the need for further advancements to develop smarter AI solutions for swine respiratory health monitoring.Porcine respiratory disease complex is an economically important disease in the swine industry. Early detection of the disease is crucial for immediate response to the disease at the farm level to prevent and minimize the potential damage that it may cause. In this paper, recent studies on the application of artificial intelligence (AI) in the early detection and monitoring of respiratory disease in swine have been reviewed. Most of the studies used coughing sounds as a feature of respiratory disease. The performance of different models and the methodologies used for cough recognition using AI were reviewed and compared. An AI technology available in the market was also reviewed. The device uses audio technology that can monitor and evaluate the herd’s respiratory health status through cough-sound recognition and quantification. The device also has temperature and humidity sensors to monitor environmental conditions. It has an alarm system based on variations in coughing patterns and abrupt temperature changes. However, some limitations of the existing technology were identified. Substantial effort must be exerted to surmount the limitations to have a smarter AI technology for monitoring respiratory health status in swine.

Nanoscale dynamic chemical, biological sensor material designs for control monitoring and early detection of advanced diseases.

Pathogenetic process monitoring and early detection of pear black spot disease caused by Alternaria alternata using hyperspectral imaging

Zoonotic diseases, which are transmitted from animals to humans, pose a significant threat to global public health and livestock productivity. Early detection of these diseases is crucial to prevent large-scale outbreaks and economic losses. This research focuses on the design and implementation of an artificial intelligence (AI)-based system for the early detection of zoonotic diseases using animal skin images. The proposed system employs advanced image processing and deep learning techniques to automatically identify visual symptoms such as lesions, rashes, or discolorations that indicate possible infections. Convolutional Neural Networks (CNNs) are utilized for feature extraction and classification of various skin conditions, distinguishing between healthy and infected animals with high accuracy. A user-friendly web or mobile interface is developed to enable farmers and veterinarians to upload images for instant diagnosis and receive recommendations for prompt intervention. Experimental results demonstrate that the system can accurately detect early signs of zoonotic diseases, thereby supporting proactive veterinary care and contributing to the prevention of disease transmission from animals to humans.

Background:Early detection of chronic kidney disease needs to be developed because the prevalence of chronic kidney disease continues to increase in Kendari City, Indonesia. Objective:The study aimed to analyse of differences in early detection of chronic kidney disease with urine proteins, creatinine, and individual health status based on behaviours, psychological-stress environment and genetic factors in Kendari City, Indonesia. Methods:This research used quantitative method with a cross sectional study approach. This study was conducted in Kendari City, Southeast Sulawesi, Indonesia, which recruited 136 subjects aged between 24-70 years. The participants were interviewed and tested urine. The dependent variables are protein-urine, creatinine, and health status. The independent variables are behaviours, psychological environment-stress and genetics. Data analysis used multinomial logistic regression statistical tests. Results: This study suggests that there are differences between tests for urine protein levels, creatinine and individual health status for early detection of chronic kidney disease which is associated with behaviours, psychological-stress environment and genetic factors in Kendari City. Protein-urine can be used early detection of chronic kidney disease which is related to daily water consumption (p=0.001, OR=1.56), calory intake (p=0.036, OR=2.13) and psychological stress environment (p=0.017, OR=0.11). However, urine creatine test cannot be use for early detection of chronic kidney disease. Meanwhile, individual’s health status can be used to early detection of chronic kidney disease with relating to daily water consumption behaviour of less than 1000 ml a day (p<0.0001, OR=1.56), physical activity (p<0.05, OR=5.7), medication adherence (P<0.01, OR=0.4), and psychological stress environment (p<0.0001, OR=8.6). Conclusion: Early detection of chronic kidney disease may be more effective by observing health status directly, or by urine protein testing, compared to urine creatinine testing. Keywords:Chronic kidney disease, behaviour, genetic, stress, proteins.

Cancer is a global major public health problem since it is a leading cause of death, accounting for nearly 10 million deaths in 2020 worldwide and the most recent epidemiological data suggested that its global impact is growing significantly. In this context, cancer survivors have to live for a long time often in a condition of disability due to the long-term consequences, both physical and psychological. These difficulties can seriously impair their working ability, limiting the employability. In this context, the occupational physician plays a key role in the implementation and enforcement of measures to support the workers affected by cancer, to address issues such as the information on health promotion, the analysis of work capacity and the management of disability at work and also promoting a timely and effective return to work and preserving their employability. Therefore, the aim of this study was to gather useful information to support the occupational physicians in the management of workers affected by cancer, through a survey on 157 Italian occupational physicians. Based on the interviewees' opinions, the most useful occupational safety and health professionals in terms of job retention and preservation of workers affected by cancer are the employers and the occupational physicians themselves, whose role is crucial in identifying and applying the most effective reasonable accommodations that should be provided to the workers affected by cancer. The provision of these accommodations take place on the occasion of mandatory health surveillance medical examination to which the worker affected by cancer is subjected when he returns to work. Results on training and information needs showed that the management of the workers affected by cancer is essentially centered on an appropriate fitness for work judgment and on the correct performance of health surveillance. However, an effective and successful management model should be based on a multidisciplinary and integrated approach that, from the earliest stages of the disease, involves the occupational physicians and employers.

Identification and Solutions for Grape Leaf Disease Using Convolutional Neural Network (CNN)

Surveillance of human and animal health is often carried out separately worldwide, which leads to the under-reporting of zoonotic and emerging diseases. Early cross-information between wildlife, domestic animal and public health sectors may reduce both exposure and cost of outbreaks. We have assessed the feasibility of a One Health Surveillance and Response System (OHSRS) in the Adadle district of Ethiopia in the Somali Region (SRS), with regard to integration into the existing regional surveillance-response system in the Somali Region of Ethiopia (SRS). To meet the objectives of a surveillance-response system, we established a One Health Surveillance and Response Unit (OHSRU) at the district level. Community Animal Health Workers, Community Health Workers (CHWs), and both human and animal health district staff and regional experts were trained together on the OHSRS. An inception workshop was held with all relevant stakeholders. To ensure the active engagement of communities in the surveillance response system, a Community-Based Emergency Fund (CBEF) and CAHW cost recovery mechanisms were established. All public and animal health staff of different administration levels were linked together. Human and animal health information was collected and shared effectively among sectors. This approach helped bridging the physical separation between the public and animal health sectors in disease surveillance in the Adadle district. Joint interventions, such as disease outbreak investigations and community awareness were initiated by the OHSRU. We demonstrated that the OHSR was successfully operationalized in Adadle districts and contributed to improving the early detection and response of zoonotic diseases. However, technical barriers, cost-effectiveness, legality of data and ethical safeguarding, along with political commitment should be addressed to effectively operationalize the OHSR in the whole region. Designing the OHSR through the existing surveillance system, engaging communities and other relevant sectors using a participatory process is an important contribution to a sustainable OHSR. One Health Impact Statement In this research work, the public and animal health sectors collaborated in data collection and initiated joint interventions. By integrating the surveillance operational costs for disease outbreak investigation and cost of public health associated with zoonotic diseases can be reduced as One Health surveillance and response lead to early detection and response to zoonotic diseases. As one health is collaborative efforts multispectral, multidisciplinary and transdisciplinary, i.e. involvement of all relevant sectors including community members and different disciplines in the launching workshop of surveillance system helped to define and agree the role of each sectors or partner’s in the One Health approach. This played a curtail role in the success of the approach. Lessons learned from this work can be used for further improvement in One Health approach in different settings.

BackgroundPerson-centered care is needed to effectively support workers with chronic health conditions. Person-centered care aims to provide care tailored to an individual person’s preferences, needs and values. To achieve this, a more active, supportive, and coaching role of occupational and insurance physicians is required. In previous research, two training programs and an e-learning training with accompanying tools that can be used in the context of person-centered occupational health care were developed to contribute to this changing role. The aim was to investigate the feasibility of the developed training programs and e-learning training to enhance the active, supportive, and coaching role of occupational and insurance physicians needed for person-centered occupational health care. Information about this is important to facilitate implementation of the tools and training into educational structures and occupational health practice.MethodsA qualitative study was conducted, with N = 29 semi-structured interviews with occupational physicians, insurance physicians, and representatives from occupational educational institutes. The aim was to elicit feasibility factors concerning the implementation, practicality and integration with regard to embedding the training programs and e-learning training in educational structures and the use of the tools and acquired knowledge and skills in occupational health care practice after following the trainings and e-learning training. Deductive analysis was conducted based on pre-selected focus areas for a feasibility study.ResultsFrom an educational perspective, adapting the face-to-face training programs to online versions, good coordination with educational managers and train-the-trainer approaches were mentioned as facilitating factors for successful implementation. Participants underlined the importance of aligning the occupational physicians’ and insurance physicians’ competences with the educational content and attention for the costs concerning the facilitation of the trainings and e-learning training. From the professional perspective, factors concerning the content of the training and e-learning training, the use of actual cases from practice, as well as follow-up training sessions were reported. Professionals expressed good fit of the acquired skills into their consultation hour in practice.ConclusionThe developed training programs, e-learning training and accompanying tools were perceived feasible in terms of implementation, practicality, and integration by occupational physicians, insurance physicians and educational institutes.

Chronic obstructive pulmonary disease (COPD) results from a complex interaction between genes and the environment, and occupational exposures are an underappreciated risk factor. Until now, little research attention has been paid to the potential impact of occupational risk factor exposure on the COPD in China. The aim of this retrospective study was to analyze the role of occupational risk factor exposure on the severity and progression of COPD for exploring new prevention strategies for this disease. This study adopted a random cluster-sampling method. Five grade-A tertiary hospitals that met the inclusion criteria were selected as the survey sites, and patients with COPD hospitalized in these hospitals from January 1, 2019, to December 31, 2019, were selected as the research subjects. Data of the patients diagnosed with COPD met the Global Initiative for Chronic Obstructive Lung Disease (2019) criteria and were collected from the computerized medical record databases. Among 4082 investigated COPD patients, 1063 (26%) were found to have occupational risk factor exposure history. The top 3 industries with a large COPD case number and a history of occupational risk factor exposure ranked in the order of agriculture (including farming, forestry, animal husbandry, and fishery), manufacturing, and mining. Further multivariate logistic regression analysis indicated that when setting a low exposure level as a reference, medium and high exposure levels were correlated with the severity of COPD (odds ratio values were 2.837 and 6.201, respectively, P < .05). Linear regression analysis showed that cumulative exposure to occupational risk factors was negatively correlated with the forced expiratory volume in 1-second percentage of COPD patients, with a correlation coefficient of 0.68. Our results indicated that occupational risk factor exposure levels were related to the severity of COPD significantly. The incubation period of COPD in the exposure group was significantly shorter than that in the non-exposure group. To prevent worked-related COPD, special attention and control efforts should be taken to reduce the level of occupational risk factors such as organic dust, irritating chemicals, etc in the work environments, especially in the industries of agriculture, forestry, animal husbandry and fishery, manufacturing, and mining.