Search

Background and Objectives: Severe pneumonia is the leading cause of death among young children worldwide, disproportionately impacting children who lack access to advanced diagnostic imaging. Here our objectives were to develop and test the accuracy of an artificial intelligence algorithm for detecting features of pulmonary consolidation on point-of-care lung ultrasounds among hospitalized children., Methods: This was a prospective, multicenter center study conducted at academic Emergency Department and Pediatric inpatient or intensive care units between 2018-2020. Pediatric participants from 18 months to 17 years old with suspicion of lower respiratory tract infection were enrolled. Bedside lung ultrasounds were performed using a Philips handheld Lumify C5-2 transducer and standardized protocol to collect video loops from twelve lung zones, and lung features at both the video and frame levels annotated. Data from both affected and unaffected lung fields were split at the participant level into training, tuning, and holdout sets used to train, tune hyperparameters, and test an algorithm for detection of consolidation features. Data collected from adults with lower respiratory tract disease were added to enrich the training set. Algorithm performance at the video level to detect consolidation on lung ultrasound was determined using reference standard diagnosis of positive or negative pneumonia derived from clinical data., Results: Data from 107 pediatric participants yielded 117 unique exams and contributed 604 positive and 589 negative videos for consolidation that were utilized for the algorithm development process. Overall accuracy for the model for identification and localization of consolidation was 88.5%, with sensitivity 88%, specificity 89%, positive predictive value 89%, and negative predictive value 87%., Conclusions: Our algorithm demonstrated high accuracy for identification of consolidation features on pediatric chest ultrasound in children with pneumonia. Automated diagnostic support on an ultraportable point-of-care device has important implications for global health, particularly in austere settings., Competing Interests: Dr. Kenton Gregory received compensation as Principal Investigator for a DARPA award to Inventive Government Solutions Inc, previously owned by Intellectual Ventures Laboratory. Dr. Kenton Gregory also received compensation as a Fellow from Intellectual Ventures Laboratory for a portion of this work. This potential conflict for Drs. Kenton and Cynthia Gregory has been reviewed and managed by OHSU. Since the time this research was completed, Global Health Labs Inc. acquired all the assets of Inventive Government Solutions and Intellectual Ventures Laboratory. Drs. Mehanian and Millin, as well as S. Kulhare and M. Horning, received salary support through Global Health Labs Inc., a non-profit organization that may have a commercial interest in the results of this research and technology. Global Health Labs Inc. has been awarded proprietary ownership of the code utilized in the algorithm. These commercial affiliations do not alter our adherence to PLOS ONE policies on sharing data and materials, (Copyright: © 2024 Kessler et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibility of field microscopy. The World Health Organization (WHO) has designed a 55-slide set (WHO 55) for their External Competence Assessment of Malaria Microscopists (ECAMM) programme, which can also serve as a valuable benchmark for automated systems. The performance of a fully-automated malaria diagnostic system, EasyScan GO, on a WHO 55 slide set was evaluated., Methods: The WHO 55 slide set is designed to evaluate microscopist competence in three areas of malaria diagnosis using Giemsa-stained blood films, focused on crucial field needs: malaria parasite detection, malaria parasite species identification (ID), and malaria parasite quantitation. The EasyScan GO is a fully-automated system that combines scanning of Giemsa-stained blood films with assessment algorithms to deliver malaria diagnoses. This system was tested on a WHO 55 slide set., Results: The EasyScan GO achieved 94.3 % detection accuracy, 82.9 % species ID accuracy, and 50 % quantitation accuracy, corresponding to WHO microscopy competence Levels 1, 2, and 1, respectively. This is, to our knowledge, the best performance of a fully-automated system on a WHO 55 set., Conclusions: EasyScan GO's expert ratings in detection and quantitation on the WHO 55 slide set point towards its potential value in drug efficacy use-cases, as well as in some case management situations with less stringent species ID needs. Improved runtime may enable use in general case management settings.

Background: Stocking unused or expired medicines or donating these to others can lead to accidental or inappropriate ingestion, increasing the risk of adverse drug reaction(s) and even lead to antibiotic resistance. Further improper disposal of expired or unused medicines is associated with environmental pollution, health hazards and damage to ecosystem. Ecopharmacovigilance is an important area in this context., Objective: To explore the awareness and disposal practices of unused/expired in the general public., Materials and Methods: The current study is an observational, cross-sectional, questionnaire-based study conducted in 956 medicine consumers in New Delhi and National Capital Region, India. The knowledge, attitude, and practice of consumers regarding the disposal of unused medicines were evaluated., Results: The majority (89.9%) of consumers opined that expiry of medicine(s) meant for completion of shelf life, production of toxic chemical and loss of or decreasing of beneficial effects of medicines. Majority (87%) of the consumers stored medicines at home. Almost all (92.6%) of the consumers threw away the expired medicines after storing for few days. Consumers discarded the expired medicines mainly in household trash (73%). The majority (93%) of consumers were in favour of a government program to collect unused or expired medicines from their home., Conclusion: The knowledge and practices of consumers towards disposal of unused and expired medicines needs to be improved. Health care professionals, government and policy makers should offer training to educate and guide the general public on safe and proper disposal practices of expired or unused medicines., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)