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Perrin Franck C, Babington-Ashaye A, Dietrich D, Bediang G, Veltsos P, Gupta PP, Juech C, Kadam R, Collin M, Setian L, Serrano Pons J, Kwankam SY, Garrette B, Barbe S, Bagayoko CO, Mehl G, Lovis C, Geissbuhler A. Correction: iCHECK-DH: Guidelines and Checklist for the Reporting on Digital Health Implementations. J Med Internet Res 2023; 25:e49027. [PMID: 37201181 PMCID: PMC10236274 DOI: 10.2196/49027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 05/15/2023] [Indexed: 05/20/2023] Open
Abstract
[This corrects the article DOI: 10.2196/46694.].
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Affiliation(s)
- Caroline Perrin Franck
- Department of Radiology and Medical InformaticsFaculty of MedicineUniversity of GenevaGenevaSwitzerland
- Geneva Digital Health HubGenevaSwitzerland
| | - Awa Babington-Ashaye
- Department of Radiology and Medical InformaticsFaculty of MedicineUniversity of GenevaGenevaSwitzerland
- Geneva Digital Health HubGenevaSwitzerland
| | | | - Georges Bediang
- Faculty of Medicine and Biomedical SciencesUniversity of Yaoundé 1YaoundéCameroon
| | | | | | - Claudia Juech
- Government InnovationBloomberg PhilanthropiesNew York, NYUnited States
| | - Rigveda Kadam
- Foundation for Innovative New DiagnosticsGenevaSwitzerland
| | | | | | | | - S Yunkap Kwankam
- International Society for Telemedicine & eHealthBaselSwitzerland
| | | | | | - Cheick Oumar Bagayoko
- Centre d’Innovation et de Santé DigitaleDigiSanté-MaliUniversité des sciences, des techniques et des technologies de BamakoBamakoMali
- Centre d’Expertise et de Recherche en Télémédecine et E-SantéBamakoMali
| | - Garrett Mehl
- Department of Digital Health and InnovationWorld Health OrganizationGenevaSwitzerland
| | - Christian Lovis
- Department of Radiology and Medical InformaticsFaculty of MedicineUniversity of GenevaGenevaSwitzerland
- Division of Medical Information SciencesGeneva University HospitalsGenevaSwitzerland
| | - Antoine Geissbuhler
- Department of Radiology and Medical InformaticsFaculty of MedicineUniversity of GenevaGenevaSwitzerland
- Geneva Digital Health HubGenevaSwitzerland
- Division of Medical Information SciencesGeneva University HospitalsGenevaSwitzerland
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Perrin Franck C, Babington-Ashaye A, Dietrich D, Bediang G, Veltsos P, Gupta PP, Juech C, Kadam R, Collin M, Setian L, Serrano Pons J, Kwankam SY, Garrette B, Barbe S, Bagayoko CO, Mehl G, Lovis C, Geissbuhler A. iCHECK-DH: Guidelines and Checklist for the Reporting on Digital Health Implementations. J Med Internet Res 2023; 25:e46694. [PMID: 37163336 PMCID: PMC10209789 DOI: 10.2196/46694] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/18/2023] [Accepted: 04/21/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND Implementation of digital health technologies has grown rapidly, but many remain limited to pilot studies due to challenges, such as a lack of evidence or barriers to implementation. Overcoming these challenges requires learning from previous implementations and systematically documenting implementation processes to better understand the real-world impact of a technology and identify effective strategies for future implementation. OBJECTIVE A group of global experts, facilitated by the Geneva Digital Health Hub, developed the Guidelines and Checklist for the Reporting on Digital Health Implementations (iCHECK-DH, pronounced "I checked") to improve the completeness of reporting on digital health implementations. METHODS A guideline development group was convened to define key considerations and criteria for reporting on digital health implementations. To ensure the practicality and effectiveness of the checklist, it was pilot-tested by applying it to several real-world digital health implementations, and adjustments were made based on the feedback received. The guiding principle for the development of iCHECK-DH was to identify the minimum set of information needed to comprehensively define a digital health implementation, to support the identification of key factors for success and failure, and to enable others to replicate it in different settings. RESULTS The result was a 20-item checklist with detailed explanations and examples in this paper. The authors anticipate that widespread adoption will standardize the quality of reporting and, indirectly, improve implementation standards and best practices. CONCLUSIONS Guidelines for reporting on digital health implementations are important to ensure the accuracy, completeness, and consistency of reported information. This allows for meaningful comparison and evaluation of results, transparency, and accountability and informs stakeholder decision-making. i-CHECK-DH facilitates standardization of the way information is collected and reported, improving systematic documentation and knowledge transfer that can lead to the development of more effective digital health interventions and better health outcomes.
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Affiliation(s)
- Caroline Perrin Franck
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Geneva Digital Health Hub, Geneva, Switzerland
| | - Awa Babington-Ashaye
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Geneva Digital Health Hub, Geneva, Switzerland
| | | | - Georges Bediang
- Faculty of Medicine and Biomedical Sciences, University of Yaoundé 1, Yaoundé, Cameroon
| | | | | | - Claudia Juech
- Government Innovation, Bloomberg Philanthropies, New York, NY, United States
| | - Rigveda Kadam
- Foundation for Innovative New Diagnostics, Geneva, Switzerland
| | | | | | | | - S Yunkap Kwankam
- International Society for Telemedicine & eHealth, Basel, Switzerland
| | | | | | - Cheick Oumar Bagayoko
- Centre d'Innovation et de Santé Digitale, DigiSanté-Mali, Université des sciences, des techniques et des technologies de Bamako, Bamako, Mali
- Centre d'Expertise et de Recherche en Télémédecine et E-Santé, Bamako, Mali
| | - Garrett Mehl
- Department of Digital Health and Innovation, World Health Organization, Geneva, Switzerland
| | - Christian Lovis
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland
| | - Antoine Geissbuhler
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Geneva Digital Health Hub, Geneva, Switzerland
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland
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Barbieri RR, Xu Y, Setian L, Souza-Santos PT, Trivedi A, Cristofono J, Bhering R, White K, Sales AM, Miller G, Nery JAC, Sharman M, Bumann R, Zhang S, Goldust M, Sarno EN, Mirza F, Cavaliero A, Timmer S, Bonfiglioli E, Smith C, Scollard D, Navarini AA, Aerts A, Ferres JL, Moraes MO. Reimagining leprosy elimination with AI analysis of a combination of skin lesion images with demographic and clinical data. Lancet Reg Health Am 2022; 9:100192. [PMID: 36776278 PMCID: PMC9903738 DOI: 10.1016/j.lana.2022.100192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Background Leprosy is an infectious disease that mostly affects underserved populations. Although it has been largely eliminated, still about 200'000 new patients are diagnosed annually. In the absence of a diagnostic test, clinical diagnosis is often delayed, potentially leading to irreversible neurological damage and its resulting stigma, as well as continued transmission. Accelerating diagnosis could significantly contribute to advancing global leprosy elimination. Digital and Artificial Intelligence (AI) driven technology has shown potential to augment health workers abilities in making faster and more accurate diagnosis, especially when using images such as in the fields of dermatology or ophthalmology. That made us start the quest for an AI-driven diagnosis assistant for leprosy, based on skin images. Methods Here we describe the accuracy of an AI-enabled image-based diagnosis assistant for leprosy, called AI4Leprosy, based on a combination of skin images and clinical data, collected following a standardized process. In a Brazilian leprosy national referral center, 222 patients with leprosy or other dermatological conditions were included, and the 1229 collected skin images and 585 sets of metadata are stored in an open-source dataset for other researchers to exploit. Findings We used this dataset to test whether a CNN-based AI algorithm could contribute to leprosy diagnosis and employed three AI models, testing images and metadata both independently and in combination. AI modeling indicated that the most important clinical signs are thermal sensitivity loss, nodules and papules, feet paresthesia, number of lesions and gender, but also scaling surface and pruritus that were negatively associated with leprosy. Using elastic-net logistic regression provided a high classification accuracy (90%) and an area under curve (AUC) of 96.46% for leprosy diagnosis. Interpretation Future validation of these models is underway, gathering larger datasets from populations of different skin types and collecting images with smartphone cameras to mimic real world settings. We hope that the results of our research will lead to clinical solutions that help accelerate global leprosy elimination. Funding This study was partially funded by Novartis Foundation and Microsoft (in-kind contribution).
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Affiliation(s)
- Raquel R Barbieri
- Laboratório de Hanseníase Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil,Corresponding authors.
| | - Yixi Xu
- Microsoft, One Microsoft Way, One Microsoft Way, Redmond, WA, United States,Corresponding authors.
| | | | - Paulo Thiago Souza-Santos
- Laboratório de Hanseníase Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | - Anusua Trivedi
- Microsoft, One Microsoft Way, One Microsoft Way, Redmond, WA, United States
| | - Jim Cristofono
- Microsoft, One Microsoft Way, One Microsoft Way, Redmond, WA, United States
| | - Ricardo Bhering
- Laboratório de Hanseníase Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | - Kevin White
- Microsoft, One Microsoft Way, One Microsoft Way, Redmond, WA, United States
| | - Anna M Sales
- Laboratório de Hanseníase Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | - Geralyn Miller
- Microsoft, One Microsoft Way, One Microsoft Way, Redmond, WA, United States
| | - José Augusto C Nery
- Laboratório de Hanseníase Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | - Michael Sharman
- Microsoft, One Microsoft Way, One Microsoft Way, Redmond, WA, United States
| | - Richard Bumann
- Microsoft, One Microsoft Way, One Microsoft Way, Redmond, WA, United States
| | - Shun Zhang
- Microsoft, One Microsoft Way, One Microsoft Way, Redmond, WA, United States
| | - Mohamad Goldust
- University of Basel, Basel, Switzerland,Department of Dermatology, University Medical Center Mainz, Mainz, Germany
| | - Euzenir N Sarno
- Laboratório de Hanseníase Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil
| | | | | | - Sander Timmer
- Microsoft, One Microsoft Way, One Microsoft Way, Redmond, WA, United States
| | - Elena Bonfiglioli
- Microsoft, One Microsoft Way, One Microsoft Way, Redmond, WA, United States
| | | | | | | | - Ann Aerts
- Novartis Foundation, Basel, Switzerland
| | - Juan Lavista Ferres
- Microsoft, One Microsoft Way, One Microsoft Way, Redmond, WA, United States,Corresponding authors.
| | - Milton O Moraes
- Laboratório de Hanseníase Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil,Corresponding authors.
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