AIMC Topic: Occupational Exposure
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Risk analysis of noise-induced hearing loss of workers in the automobile manufacturing industries based on back-propagation neural network model: a cross-sectional study in Han Chinese population.
BMJ open
May 17, 2024
OBJECTIVES: This study aims to predict the risk of noise-induced hearing loss (NIHL) through a back-propagation neural network (BPNN) model. It provides an early, simple and accurate prediction method for NIHL.
Predicting noise-induced hearing loss with machine learning: the influence of tinnitus as a predictive factor.
The Journal of laryngology and otology
May 9, 2024
OBJECTIVES: This study aimed to determine which machine learning model is most suitable for predicting noise-induced hearing loss and the effect of tinnitus on the models' accuracy.
Responsible Development of Emerging Technologies: Extensions and Lessons From Nanotechnology for Worker Protection.
Journal of occupational and environmental medicine
Mar 27, 2024
OBJECTIVES: This paper identifies approaches to the responsible development of emerging technologies to secure worker safety and health.
Deep Learning Neural Network-Guided Detection of Asbestos Bodies in Bronchoalveolar Lavage Samples.
Acta cytologica
Sep 19, 2023
INTRODUCTION: Asbestos is a global occupational health hazard, and exposure to it by inhalation predisposes to interstitial as well as malignant pulmonary morbidity. Over time, asbestos fibers embedded in lung tissue can become coated with iron-rich ...
Evaluation of cancer drug infusion devices prior to the implementation of a compounding robot.
Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
May 18, 2023
INTRODUCTION: Compounding robots are increasingly being implemented in hospital pharmacies. In our hospital, the recent acquisition of a robot (RIVA, ARxIUM) for intravenous cancer drug compounding obliged us to replace the previously used infusion d...
Deep learning for asbestos counting.
Journal of hazardous materials
May 6, 2023
The PCM (phase contrast microscopy) method for asbestos counting needs special sample treatments, hence it is time consuming and rather expensive. As an alternative, we implemented a deep learning procedure on images directly acquired from the untrea...
Deep Learning for the Automatic Quantification of Pleural Plaques in Asbestos-Exposed Subjects.
International journal of environmental research and public health
Jan 27, 2022
OBJECTIVE: This study aimed to develop and validate an automated artificial intelligence (AI)-driven quantification of pleural plaques in a population of retired workers previously occupationally exposed to asbestos.
Radiation Protection and Occupational Exposure on Ga-PSMA-11-Based Cerenkov Luminescence Imaging Procedures in Robot-Assisted Prostatectomy.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Dec 16, 2021
Cerenkov luminescence imaging (CLI) was successfully implemented in the intraoperative context as a form of radioguided cancer surgery, showing promise in the detection of surgical margins during robot-assisted radical prostatectomy. The present stud...
Evaluation of external contamination on the vial surfaces of some hazardous drugs that commonly used in Chinese hospitals and comparison between environmental contamination generated during robotic compounding by IV: Dispensing robot vs. manual compounding in biological safety cabinet.
Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
Jun 24, 2021
OBJECTIVES: The aims of the study were to evaluate the external contamination of hazardous drug vials used in Chinese hospitals and to compare environmental contamination generated by a robotic intelligent dispensing system (WEINAS) and a manual comp...
Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography.
Occupational and environmental medicine
May 29, 2020
OBJECTIVES: To investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.
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