Artificial intelligence in mobile health for skin cancer diagnostics at home (AIM HIGH): a pilot feasibility study

Artificial intelligence (AI)-based mobile phone apps (mHealth) have the potential to streamline care for suspicious skin lesions in primary care. This study aims to investigate the conditions and feasibility of a study that incorporates an AI-based app in primary care and evaluates its potential imp...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:EClinicalMedicine 2023-06, Vol.60, p.102019-102019, Article 102019
Hauptverfasser: Smak Gregoor, Anna M., Sangers, Tobias E., Eekhof, Just AH, Howe, Sydney, Revelman, Jeroen, Litjens, Romy JM, Sarac, Mohammed, Bindels, Patrick JE, Bonten, Tobias, Wehrens, Rik, Wakkee, Marlies
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 102019
container_issue
container_start_page 102019
container_title EClinicalMedicine
container_volume 60
creator Smak Gregoor, Anna M.
Sangers, Tobias E.
Eekhof, Just AH
Howe, Sydney
Revelman, Jeroen
Litjens, Romy JM
Sarac, Mohammed
Bindels, Patrick JE
Bonten, Tobias
Wehrens, Rik
Wakkee, Marlies
description Artificial intelligence (AI)-based mobile phone apps (mHealth) have the potential to streamline care for suspicious skin lesions in primary care. This study aims to investigate the conditions and feasibility of a study that incorporates an AI-based app in primary care and evaluates its potential impact. We conducted a pilot feasibility study from November 22nd, 2021 to June 9th, 2022 with a mixed-methods design on implementation of an AI-based mHealth app for skin cancer detection in three primary care practices in the Netherlands (Rotterdam, Leiden and Katwijk). The primary outcome was the inclusion and successful participation rate of patients and general practitioners (GPs). Secondary outcomes were the reasons, facilitators and barriers for successful participation and the potential impact in both pathways for future sample size calculations. Patients were offered use of an AI-based mHealth app before consulting their GP. GPs assessed the patients blinded and then unblinded to the app. Qualitative data included observations and audio-diaries from patients and GPs and focus-groups and interviews with GPs and GP assistants. Fifty patients were included with a median age of 52 years (IQR 33.5–60.3), 64% were female, and 90% had a light skin type. The average patient inclusion rate was 4–6 per GP practice per month and 84% (n = 42) successfully participated. Similarly, in 90% (n = 45 patients) the GPs also successfully completed the study. GPs never changed their working diagnosis, but did change their treatment plan (n = 5) based on the app's assessments. Notably, 54% of patients with a benign skin lesion and low risk rating, indicated that they would be reassured and cancel their GP visit with these results (p 
doi_str_mv 10.1016/j.eclinm.2023.102019
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10227364</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S2589537023001967</els_id><sourcerecordid>2821642662</sourcerecordid><originalsourceid>FETCH-LOGICAL-c464t-d5ac3da2deb50eb535bc40f27544910421fdb164c8fdd08d5e60dc9d6755cc283</originalsourceid><addsrcrecordid>eNp9kcFuEzEQhi0EolXpGyDkYzkkeL22d5cDKKqgiVTEBc6W155NJnjXwXYq5e1xtKUqFw6W7Zl__hnNR8jbii0rVqkP-yVYj9O45IzXJcRZ1b0gl1y23ULWDXv57H1BrlPaM1ZEou0Ue00u6oarqubiksRVzDigReMpThm8xy1MFsqHjqFHD3QHxucdHUKk6VcJW1PykTo02ymkjDZRk-kujEBvVptvdL25W7__SA09oA-ZDmASFiPMJ5ry0Z3ekFeD8QmuH-8r8vPrlx-368X997vN7ep-YYUSeeGksbUz3EEvWTm17K1gA2-kEF3FBK8G11dK2HZwjrVOgmLOdk41UlrL2_qKfJ59D8d-BGdhytF4fYg4mnjSwaD-NzPhTm_Dgy7r5E2tRHG4eXSI4fcRUtYjJlt2ZCYIx6R5y8sAXClepGKW2hhSijA89amYPiPTez0j02dkekZWyt49n_Gp6C-gIvg0C6Bs6gEh6mTxDMhhBJu1C_j_Dn8A0PWqjw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2821642662</pqid></control><display><type>article</type><title>Artificial intelligence in mobile health for skin cancer diagnostics at home (AIM HIGH): a pilot feasibility study</title><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><creator>Smak Gregoor, Anna M. ; Sangers, Tobias E. ; Eekhof, Just AH ; Howe, Sydney ; Revelman, Jeroen ; Litjens, Romy JM ; Sarac, Mohammed ; Bindels, Patrick JE ; Bonten, Tobias ; Wehrens, Rik ; Wakkee, Marlies</creator><creatorcontrib>Smak Gregoor, Anna M. ; Sangers, Tobias E. ; Eekhof, Just AH ; Howe, Sydney ; Revelman, Jeroen ; Litjens, Romy JM ; Sarac, Mohammed ; Bindels, Patrick JE ; Bonten, Tobias ; Wehrens, Rik ; Wakkee, Marlies</creatorcontrib><description>Artificial intelligence (AI)-based mobile phone apps (mHealth) have the potential to streamline care for suspicious skin lesions in primary care. This study aims to investigate the conditions and feasibility of a study that incorporates an AI-based app in primary care and evaluates its potential impact. We conducted a pilot feasibility study from November 22nd, 2021 to June 9th, 2022 with a mixed-methods design on implementation of an AI-based mHealth app for skin cancer detection in three primary care practices in the Netherlands (Rotterdam, Leiden and Katwijk). The primary outcome was the inclusion and successful participation rate of patients and general practitioners (GPs). Secondary outcomes were the reasons, facilitators and barriers for successful participation and the potential impact in both pathways for future sample size calculations. Patients were offered use of an AI-based mHealth app before consulting their GP. GPs assessed the patients blinded and then unblinded to the app. Qualitative data included observations and audio-diaries from patients and GPs and focus-groups and interviews with GPs and GP assistants. Fifty patients were included with a median age of 52 years (IQR 33.5–60.3), 64% were female, and 90% had a light skin type. The average patient inclusion rate was 4–6 per GP practice per month and 84% (n = 42) successfully participated. Similarly, in 90% (n = 45 patients) the GPs also successfully completed the study. GPs never changed their working diagnosis, but did change their treatment plan (n = 5) based on the app's assessments. Notably, 54% of patients with a benign skin lesion and low risk rating, indicated that they would be reassured and cancel their GP visit with these results (p &lt; 0.001). Our findings suggest that studying implementation of an AI-based mHealth app for detection of skin cancer in the hands of patients or as a diagnostic tool used by GPs in primary care appears feasible. Preliminary results indicate potential to further investigate both intended use settings. SkinVision B.V.</description><identifier>ISSN: 2589-5370</identifier><identifier>EISSN: 2589-5370</identifier><identifier>DOI: 10.1016/j.eclinm.2023.102019</identifier><identifier>PMID: 37261324</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Artificial intelligence ; Convolutional neural network ; General practitioners ; Mobile health ; Primary care ; Skin cancer</subject><ispartof>EClinicalMedicine, 2023-06, Vol.60, p.102019-102019, Article 102019</ispartof><rights>2023 The Author(s)</rights><rights>2023 The Author(s).</rights><rights>2023 The Author(s) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c464t-d5ac3da2deb50eb535bc40f27544910421fdb164c8fdd08d5e60dc9d6755cc283</citedby><cites>FETCH-LOGICAL-c464t-d5ac3da2deb50eb535bc40f27544910421fdb164c8fdd08d5e60dc9d6755cc283</cites><orcidid>0000-0001-8578-901X ; 0000-0002-7997-8532</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227364/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227364/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37261324$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Smak Gregoor, Anna M.</creatorcontrib><creatorcontrib>Sangers, Tobias E.</creatorcontrib><creatorcontrib>Eekhof, Just AH</creatorcontrib><creatorcontrib>Howe, Sydney</creatorcontrib><creatorcontrib>Revelman, Jeroen</creatorcontrib><creatorcontrib>Litjens, Romy JM</creatorcontrib><creatorcontrib>Sarac, Mohammed</creatorcontrib><creatorcontrib>Bindels, Patrick JE</creatorcontrib><creatorcontrib>Bonten, Tobias</creatorcontrib><creatorcontrib>Wehrens, Rik</creatorcontrib><creatorcontrib>Wakkee, Marlies</creatorcontrib><title>Artificial intelligence in mobile health for skin cancer diagnostics at home (AIM HIGH): a pilot feasibility study</title><title>EClinicalMedicine</title><addtitle>EClinicalMedicine</addtitle><description>Artificial intelligence (AI)-based mobile phone apps (mHealth) have the potential to streamline care for suspicious skin lesions in primary care. This study aims to investigate the conditions and feasibility of a study that incorporates an AI-based app in primary care and evaluates its potential impact. We conducted a pilot feasibility study from November 22nd, 2021 to June 9th, 2022 with a mixed-methods design on implementation of an AI-based mHealth app for skin cancer detection in three primary care practices in the Netherlands (Rotterdam, Leiden and Katwijk). The primary outcome was the inclusion and successful participation rate of patients and general practitioners (GPs). Secondary outcomes were the reasons, facilitators and barriers for successful participation and the potential impact in both pathways for future sample size calculations. Patients were offered use of an AI-based mHealth app before consulting their GP. GPs assessed the patients blinded and then unblinded to the app. Qualitative data included observations and audio-diaries from patients and GPs and focus-groups and interviews with GPs and GP assistants. Fifty patients were included with a median age of 52 years (IQR 33.5–60.3), 64% were female, and 90% had a light skin type. The average patient inclusion rate was 4–6 per GP practice per month and 84% (n = 42) successfully participated. Similarly, in 90% (n = 45 patients) the GPs also successfully completed the study. GPs never changed their working diagnosis, but did change their treatment plan (n = 5) based on the app's assessments. Notably, 54% of patients with a benign skin lesion and low risk rating, indicated that they would be reassured and cancel their GP visit with these results (p &lt; 0.001). Our findings suggest that studying implementation of an AI-based mHealth app for detection of skin cancer in the hands of patients or as a diagnostic tool used by GPs in primary care appears feasible. Preliminary results indicate potential to further investigate both intended use settings. SkinVision B.V.</description><subject>Artificial intelligence</subject><subject>Convolutional neural network</subject><subject>General practitioners</subject><subject>Mobile health</subject><subject>Primary care</subject><subject>Skin cancer</subject><issn>2589-5370</issn><issn>2589-5370</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kcFuEzEQhi0EolXpGyDkYzkkeL22d5cDKKqgiVTEBc6W155NJnjXwXYq5e1xtKUqFw6W7Zl__hnNR8jbii0rVqkP-yVYj9O45IzXJcRZ1b0gl1y23ULWDXv57H1BrlPaM1ZEou0Ue00u6oarqubiksRVzDigReMpThm8xy1MFsqHjqFHD3QHxucdHUKk6VcJW1PykTo02ymkjDZRk-kujEBvVptvdL25W7__SA09oA-ZDmASFiPMJ5ry0Z3ekFeD8QmuH-8r8vPrlx-368X997vN7ep-YYUSeeGksbUz3EEvWTm17K1gA2-kEF3FBK8G11dK2HZwjrVOgmLOdk41UlrL2_qKfJ59D8d-BGdhytF4fYg4mnjSwaD-NzPhTm_Dgy7r5E2tRHG4eXSI4fcRUtYjJlt2ZCYIx6R5y8sAXClepGKW2hhSijA89amYPiPTez0j02dkekZWyt49n_Gp6C-gIvg0C6Bs6gEh6mTxDMhhBJu1C_j_Dn8A0PWqjw</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Smak Gregoor, Anna M.</creator><creator>Sangers, Tobias E.</creator><creator>Eekhof, Just AH</creator><creator>Howe, Sydney</creator><creator>Revelman, Jeroen</creator><creator>Litjens, Romy JM</creator><creator>Sarac, Mohammed</creator><creator>Bindels, Patrick JE</creator><creator>Bonten, Tobias</creator><creator>Wehrens, Rik</creator><creator>Wakkee, Marlies</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-8578-901X</orcidid><orcidid>https://orcid.org/0000-0002-7997-8532</orcidid></search><sort><creationdate>20230601</creationdate><title>Artificial intelligence in mobile health for skin cancer diagnostics at home (AIM HIGH): a pilot feasibility study</title><author>Smak Gregoor, Anna M. ; Sangers, Tobias E. ; Eekhof, Just AH ; Howe, Sydney ; Revelman, Jeroen ; Litjens, Romy JM ; Sarac, Mohammed ; Bindels, Patrick JE ; Bonten, Tobias ; Wehrens, Rik ; Wakkee, Marlies</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c464t-d5ac3da2deb50eb535bc40f27544910421fdb164c8fdd08d5e60dc9d6755cc283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial intelligence</topic><topic>Convolutional neural network</topic><topic>General practitioners</topic><topic>Mobile health</topic><topic>Primary care</topic><topic>Skin cancer</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Smak Gregoor, Anna M.</creatorcontrib><creatorcontrib>Sangers, Tobias E.</creatorcontrib><creatorcontrib>Eekhof, Just AH</creatorcontrib><creatorcontrib>Howe, Sydney</creatorcontrib><creatorcontrib>Revelman, Jeroen</creatorcontrib><creatorcontrib>Litjens, Romy JM</creatorcontrib><creatorcontrib>Sarac, Mohammed</creatorcontrib><creatorcontrib>Bindels, Patrick JE</creatorcontrib><creatorcontrib>Bonten, Tobias</creatorcontrib><creatorcontrib>Wehrens, Rik</creatorcontrib><creatorcontrib>Wakkee, Marlies</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>EClinicalMedicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Smak Gregoor, Anna M.</au><au>Sangers, Tobias E.</au><au>Eekhof, Just AH</au><au>Howe, Sydney</au><au>Revelman, Jeroen</au><au>Litjens, Romy JM</au><au>Sarac, Mohammed</au><au>Bindels, Patrick JE</au><au>Bonten, Tobias</au><au>Wehrens, Rik</au><au>Wakkee, Marlies</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence in mobile health for skin cancer diagnostics at home (AIM HIGH): a pilot feasibility study</atitle><jtitle>EClinicalMedicine</jtitle><addtitle>EClinicalMedicine</addtitle><date>2023-06-01</date><risdate>2023</risdate><volume>60</volume><spage>102019</spage><epage>102019</epage><pages>102019-102019</pages><artnum>102019</artnum><issn>2589-5370</issn><eissn>2589-5370</eissn><abstract>Artificial intelligence (AI)-based mobile phone apps (mHealth) have the potential to streamline care for suspicious skin lesions in primary care. This study aims to investigate the conditions and feasibility of a study that incorporates an AI-based app in primary care and evaluates its potential impact. We conducted a pilot feasibility study from November 22nd, 2021 to June 9th, 2022 with a mixed-methods design on implementation of an AI-based mHealth app for skin cancer detection in three primary care practices in the Netherlands (Rotterdam, Leiden and Katwijk). The primary outcome was the inclusion and successful participation rate of patients and general practitioners (GPs). Secondary outcomes were the reasons, facilitators and barriers for successful participation and the potential impact in both pathways for future sample size calculations. Patients were offered use of an AI-based mHealth app before consulting their GP. GPs assessed the patients blinded and then unblinded to the app. Qualitative data included observations and audio-diaries from patients and GPs and focus-groups and interviews with GPs and GP assistants. Fifty patients were included with a median age of 52 years (IQR 33.5–60.3), 64% were female, and 90% had a light skin type. The average patient inclusion rate was 4–6 per GP practice per month and 84% (n = 42) successfully participated. Similarly, in 90% (n = 45 patients) the GPs also successfully completed the study. GPs never changed their working diagnosis, but did change their treatment plan (n = 5) based on the app's assessments. Notably, 54% of patients with a benign skin lesion and low risk rating, indicated that they would be reassured and cancel their GP visit with these results (p &lt; 0.001). Our findings suggest that studying implementation of an AI-based mHealth app for detection of skin cancer in the hands of patients or as a diagnostic tool used by GPs in primary care appears feasible. Preliminary results indicate potential to further investigate both intended use settings. SkinVision B.V.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>37261324</pmid><doi>10.1016/j.eclinm.2023.102019</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-8578-901X</orcidid><orcidid>https://orcid.org/0000-0002-7997-8532</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2589-5370
ispartof EClinicalMedicine, 2023-06, Vol.60, p.102019-102019, Article 102019
issn 2589-5370
2589-5370
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10227364
source DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Alma/SFX Local Collection
subjects Artificial intelligence
Convolutional neural network
General practitioners
Mobile health
Primary care
Skin cancer
title Artificial intelligence in mobile health for skin cancer diagnostics at home (AIM HIGH): a pilot feasibility study
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T19%3A53%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Artificial%20intelligence%20in%20mobile%20health%20for%20skin%20cancer%20diagnostics%20at%20home%20(AIM%20HIGH):%20a%20pilot%20feasibility%20study&rft.jtitle=EClinicalMedicine&rft.au=Smak%20Gregoor,%20Anna%20M.&rft.date=2023-06-01&rft.volume=60&rft.spage=102019&rft.epage=102019&rft.pages=102019-102019&rft.artnum=102019&rft.issn=2589-5370&rft.eissn=2589-5370&rft_id=info:doi/10.1016/j.eclinm.2023.102019&rft_dat=%3Cproquest_pubme%3E2821642662%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2821642662&rft_id=info:pmid/37261324&rft_els_id=S2589537023001967&rfr_iscdi=true