Iterative Development, Validation, and Certification of a Smartphone System to Assess Neonatal Jaundice: Development and Usability Study

Journals

1. Kihara T, Sugihara T, Ikeda S, Matsuki Y, Koda H, Onoyama T, Takata T, Nagahara T, Isomoto H. Identification and Quantification of Jaundice by Trans-Conjunctiva Optical Imaging Using a Human Brain-like Algorithm: A Cross-Sectional Study. Diagnostics 2023;13(10):1767 View
2. Jiménez-Díaz G, Aune A, Elizarrarás-Rivas J, Gierman L, Keitsch M, Marcuzzi A, Infanti J. Neonatal jaundice detection in low-resource Mexican settings: possibilities and barriers for innovation with mobile health. BMC Health Services Research 2024;24(1) View
3. Omol E, Mburu L, Abuonji P. Digital Maturity Assessment Model (DMAM): assimilation of Design Science Research (DSR) and Capability Maturity Model Integration (CMMI). Digital Transformation and Society 2024 View
4. Bancone G, Gilder M, Win E, Gornsawun G, Moo P, Archasuksan L, Wai N, Win S, Hanboonkunupakarn B, Nosten F, Carrara V, McGready R. Non-invasive detection of bilirubin concentrations during the first week of life in a low-resource setting along the Thailand–Myanmar border. BMJ Paediatrics Open 2024;8(1):e002754 View
5. van Kempen E, Vrijlandt S, van der Geest K, Lotgering S, Hueting T, Oostenbrink R. A Blueprint for Clinical-Driven Medical Device Development: The Feverkidstool Application to Identify Children With Serious Bacterial Infection. Mayo Clinic Proceedings: Digital Health 2024 View