Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study
BackgroundDiabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluat...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2024-03-01
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| Series: | JMIR Research Protocols |
| Online Access: | https://www.researchprotocols.org/2024/1/e50568 |
| _version_ | 1827307521782579200 |
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| author | Trystan Macdonald Jacqueline Dinnes Gregory Maniatopoulos Sian Taylor-Phillips Bethany Shinkins Jeffry Hogg John Kevin Dunbar Ameenat Lola Solebo Hannah Sutton John Attwood Michael Pogose Rosalind Given-Wilson Felix Greaves Carl Macrae Russell Pearson Daniel Bamford Adnan Tufail Xiaoxuan Liu Alastair K Denniston |
| author_facet | Trystan Macdonald Jacqueline Dinnes Gregory Maniatopoulos Sian Taylor-Phillips Bethany Shinkins Jeffry Hogg John Kevin Dunbar Ameenat Lola Solebo Hannah Sutton John Attwood Michael Pogose Rosalind Given-Wilson Felix Greaves Carl Macrae Russell Pearson Daniel Bamford Adnan Tufail Xiaoxuan Liu Alastair K Denniston |
| author_sort | Trystan Macdonald |
| collection | DOAJ |
| description |
BackgroundDiabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation.
ObjectiveThis study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England.
MethodsThis work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence’s Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from “definitely exclude” to “definitely include,” and suggest edits. The document will be iterated between rounds based on participants’ feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote.
ResultsPhase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024.
ConclusionsThe multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas.
International Registered Report Identifier (IRRID)DERR1-10.2196/50568 |
| first_indexed | 2024-04-24T18:40:27Z |
| format | Article |
| id | doaj.art-eda9023eaf2743fe8f11f3c02eca03f3 |
| institution | Directory Open Access Journal |
| issn | 1929-0748 |
| language | English |
| last_indexed | 2024-04-24T18:40:27Z |
| publishDate | 2024-03-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Research Protocols |
| spelling | doaj.art-eda9023eaf2743fe8f11f3c02eca03f32024-03-27T13:15:32ZengJMIR PublicationsJMIR Research Protocols1929-07482024-03-0113e5056810.2196/50568Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods StudyTrystan Macdonaldhttps://orcid.org/0000-0002-8561-0718Jacqueline Dinneshttps://orcid.org/0000-0003-1343-7335Gregory Maniatopouloshttps://orcid.org/0000-0002-9602-3885Sian Taylor-Phillipshttps://orcid.org/0000-0002-1841-4346Bethany Shinkinshttps://orcid.org/0000-0001-5350-1018Jeffry Hogghttps://orcid.org/0000-0001-8044-7790John Kevin Dunbarhttps://orcid.org/0000-0002-4562-5851Ameenat Lola Solebohttps://orcid.org/0000-0002-8933-5864Hannah Suttonhttps://orcid.org/0009-0006-1504-1296John Attwoodhttps://orcid.org/0009-0007-9111-2233Michael Pogosehttps://orcid.org/0000-0002-6095-1639Rosalind Given-Wilsonhttps://orcid.org/0000-0002-0151-480XFelix Greaveshttps://orcid.org/0000-0001-9393-3122Carl Macraehttps://orcid.org/0000-0003-3198-7808Russell Pearsonhttps://orcid.org/0009-0003-9132-8633Daniel Bamfordhttps://orcid.org/0009-0005-5628-5628Adnan Tufailhttps://orcid.org/0000-0001-6131-7640Xiaoxuan Liuhttps://orcid.org/0000-0002-1286-0038Alastair K Dennistonhttps://orcid.org/0000-0001-7849-0087 BackgroundDiabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. ObjectiveThis study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. MethodsThis work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence’s Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from “definitely exclude” to “definitely include,” and suggest edits. The document will be iterated between rounds based on participants’ feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. ResultsPhase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. ConclusionsThe multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas. International Registered Report Identifier (IRRID)DERR1-10.2196/50568https://www.researchprotocols.org/2024/1/e50568 |
| spellingShingle | Trystan Macdonald Jacqueline Dinnes Gregory Maniatopoulos Sian Taylor-Phillips Bethany Shinkins Jeffry Hogg John Kevin Dunbar Ameenat Lola Solebo Hannah Sutton John Attwood Michael Pogose Rosalind Given-Wilson Felix Greaves Carl Macrae Russell Pearson Daniel Bamford Adnan Tufail Xiaoxuan Liu Alastair K Denniston Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study JMIR Research Protocols |
| title | Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study |
| title_full | Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study |
| title_fullStr | Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study |
| title_full_unstemmed | Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study |
| title_short | Target Product Profile for a Machine Learning–Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study |
| title_sort | target product profile for a machine learning automated retinal imaging analysis software for use in english diabetic eye screening protocol for a mixed methods study |
| url | https://www.researchprotocols.org/2024/1/e50568 |
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