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394,483 results

12. A two-arm, randomised feasibility trial using link workers to improve dental visiting in people with severe mental illness: a protocol paper

Author

Hilton, Claire, Morris, Abigail, Burnside, Girvan, Harris, Rebecca, Aggarwal, Vishal R., Procter, Sarah, Griffiths, Robert, French, Paul, Laverty, Louise, Lobban, Fiona, Berry, Katherine, Shiers, David, Golby, Rebecca, Fazekas, Fanni, Valemis, Kyriakos, Perry, Antonia, Newens, Connie, Kerry, Eirian, Mupinga, Pauline, Gkioni, Efstathia, Lodge, Christopher, Dawber, Alison, Elliott, Emma, Lunat, Farah, and Palmier-Claus, Jasper

Published
2023
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30. Community centred co-design methodology for designing and implementing socio-behavioural interventions to counter COVID-19 related misinformation among marginalized population living in the squatter settlements of Karachi, Pakistan: a methodology paper

Author

Qasim, Rubina, Farooqui, Waqas Ahmed, Rahman, Atiya, Haroon, Rukhsana, Saleem, Madiha, Rafique, Muhammad, Noor, Fiza, Ghani, Afifa, Yaqoob, Muhammad, Yadav, Uday Narayan, and Yousafzai, Mohammad T.

Published
2023
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33. Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries.

Author

Folks, Ryan D., Naik, Bhiken I., Brown, Donald E., and Durieux, Marcel E.

Subjects
*MEDICAL records, *ARTIFICIAL neural networks, *COMPUTER vision, *DIASTOLIC blood pressure, *MEDICAL personnel, *DEEP learning, *SYSTOLIC blood pressure
Abstract

Background: In low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data. Methods: We implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software. Results: The model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%. Conclusions: We demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries. [ABSTRACT FROM AUTHOR]

Published
2024
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