{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:50:02Z","timestamp":1747216202948,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685366"}],"license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,30]]},"abstract":"<jats:p>Introduction: Parkinson\u2019s disease represents a burdensome condition with complex manifestations. A licensed, standardized paper-based questionnaire is completed by both patients and physicians to monitor the progression and state of the disease. However, integrating the obtained scores into digital systems still poses a challenge. Methods: Paper-based handwriting is intuitive and an efficient mode of human-computer interaction. Accordingly, we transformed a consumer-grade tablet into a device where an exact digital copy of the disease-specific questionnaire can be filled with the supplied pen. Utilizing a small convolutional neural network directly on the device and trained on MNIST data, we translated the handwritten digits to appropriate LOINC codes and made them accessible through a FHIR-compatible HTTP interface. Results: When evaluating the usability from a patient-centric point of view, the System Usability Score revealed an excellent rating (SUS = 83.01) from the participants. However, we identified some challenges associated with the magnetic pen and the flat design of the device. Conclusion: In setups where certified medical devices are not required, consumer hardware can be used to map handwritten digits of patients to appropriate medical standards without manual intervention through healthcare professionals.<\/jats:p>","DOI":"10.3233\/shti240870","type":"book-chapter","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T09:13:29Z","timestamp":1725527609000},"source":"Crossref","is-referenced-by-count":0,"title":["Digitalizing Handwritten Digits of Patients with Parkinson\u2019s Disease Utilizing Consumer Hardware and Open-Source Software"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9301-8872","authenticated-orcid":false,"given":"Christopher","family":"Gundler","sequence":"first","affiliation":[{"name":"Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander Johannes","family":"Wiederhold","sequence":"additional","affiliation":[{"name":"Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Monika","family":"P\u00f6tter-Nerger","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T09:13:30Z","timestamp":1725527610000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"ISBN":["9781643685366"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240870","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,8,30]]}}}