scholarly journals Artificial Intelligence Support for Skin Lesion Triage in Primary Care and Dermatology

Iproceedings ◽  
10.2196/35395 ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. e35395
Author(s):  
Harmony Thompson ◽  
Amanda Oakley ◽  
Michael B Jameson ◽  
Adrian Bowling

Background Primary care providers, dermatology specialists, and health care access are key components of primary prevention, early diagnosis, and treatment of skin cancer. Artificial intelligence (AI) offers the promise of diagnostic support for nonspecialists, but real-world clinical validation of AI in primary care is lacking. Objective We aimed to (1) assess the reliability of an AI-based clinical triage algorithm in classifying benign and malignant skin lesions and (2) evaluate the quality of images obtained in primary care using the study camera (3Gen DermLite Cam v4 or similar). Methods This was a single-center, prospective, double-blinded observational study with a predetermined study design. We recruited participants with suspected skin cancer in 20 primary care practices who were referred for assessment via teledermatology. A second set of photographs taken using a standardized camera was processed by the AI algorithm. We evaluated the image quality and compared two teledermatologists’ diagnoses by consensus (the “gold standard”) with AI and histology where applicable. Results Our primary outcome assessment stratified 391 skin lesions by management as benign, uncertain, or malignant. Uncertain lesions were not included in the sensitivity and specificity analyses. Uncertain lesions included lesions that had either diagnostic or management uncertainties. For the remaining 242 lesions, the sensitivity was 97.26% (95% CI 93.13%-99.25%) and the specificity was 97.92% (95% CI 92.68%-99.75%). The AI algorithm was compared with the histological diagnoses for 123 lesions. The sensitivity was 100% (95% CI 95.85%-100%) and the specificity was 72.22% (95% CI 54.81%-85.80%). Conclusions The AI algorithm demonstrates encouraging results, with high sensitivity and specificity, concordant with previous AI studies. It shows potential as a triage tool in conjunction with teledermatology to augment health care and improve access to dermatology. Further real-life studies need to be conducted on a larger scale to assess the reliability, usability, and cost-effectiveness of the algorithm in primary care. Acknowledgments MoleMap NZ, who developed the AI algorithm, provided some funding for this study. HT's salary was partially sponsored by MoleMap NZ, who developed the AI algorithm. AB is a shareholder and consultant to Molemap Ltd provider of the AI algorithm. Conflicts of Interest None declared.


2021 ◽  
Author(s):  
Harmony Thompson ◽  
Amanda Oakley ◽  
Michael B Jameson ◽  
Adrian Bowling

BACKGROUND Primary care providers, dermatology specialists, and health care access are key components of primary prevention, early diagnosis, and treatment of skin cancer. Artificial intelligence (AI) offers the promise of diagnostic support for nonspecialists, but real-world clinical validation of AI in primary care is lacking. OBJECTIVE We aimed to (1) assess the reliability of an AI-based clinical triage algorithm in classifying benign and malignant skin lesions and (2) evaluate the quality of images obtained in primary care using the study camera (3Gen DermLite Cam v4 or similar). METHODS This was a single-center, prospective, double-blinded observational study with a predetermined study design. We recruited participants with suspected skin cancer in 20 primary care practices who were referred for assessment via teledermatology. A second set of photographs taken using a standardized camera was processed by the AI algorithm. We evaluated the image quality and compared two teledermatologists’ diagnoses by consensus (the “gold standard”) with AI and histology where applicable. RESULTS Our primary outcome assessment stratified 391 skin lesions by management as benign, uncertain, or malignant. Uncertain lesions were not included in the sensitivity and specificity analyses. Uncertain lesions included lesions that had either diagnostic or management uncertainties. For the remaining 242 lesions, the sensitivity was 97.26% (95% CI 93.13%-99.25%) and the specificity was 97.92% (95% CI 92.68%-99.75%). The AI algorithm was compared with the histological diagnoses for 123 lesions. The sensitivity was 100% (95% CI 95.85%-100%) and the specificity was 72.22% (95% CI 54.81%-85.80%). CONCLUSIONS The AI algorithm demonstrates encouraging results, with high sensitivity and specificity, concordant with previous AI studies. It shows potential as a triage tool in conjunction with teledermatology to augment health care and improve access to dermatology. Further real-life studies need to be conducted on a larger scale to assess the reliability, usability, and cost-effectiveness of the algorithm in primary care.



SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A294-A294
Author(s):  
Ivan Vargas ◽  
Alexandria Muench ◽  
Mark Seewald ◽  
Cecilia Livesey ◽  
Matthew Press ◽  
...  

Abstract Introduction Past epidemiological research indicates that insomnia and depression are both highly prevalent and tend to co-occur in the general population. The present study further assesses this association by estimating: (1) the concurrence rates of insomnia and depression in outpatients referred by their primary care providers for mental health care; and (2) whether the association between depression and insomnia varies by insomnia subtype (initial, middle, and late). Methods Data were collected from 3,174 patients (mean age=42.7; 74% women; 50% Black) who were referred to the integrated care program for assessment of mental health symptoms (2018–2020). All patients completed an Insomnia Severity Index (ISI) and a Patient Health Questionnaire (PHQ-9) during their evaluations. Total scores for the ISI and PHQ-9 were computed. These scores were used to categorize patients into diagnostic groups for insomnia (no-insomnia [ISI < 8], subthreshold-insomnia [ISI 8–14], and clinically-significant-insomnia [ISI>14]) and depression (no-depression [PHQ-914]). Items 1–3 of the ISI were also used to assess the association between depression and subtypes of insomnia. Results Rates of insomnia were as follows: 34.6% for subthreshold-insomnia, 35.5% for clinically-significant insomnia, and 28.9% for mild-depression and 26.9% for clinically-significant-depression. 92% of patients with clinically significant depression reported at least subthreshold levels of insomnia. While the majority of patients with clinical depression reported having insomnia, the proportion of patients that endorsed these symptoms were comparable across insomnia subtypes (percent by subtype: initial insomnia 63%; middle insomnia 61%; late insomnia 59%). Conclusion According to these data, the proportion of outpatients referred for mental health evaluations that endorse treatable levels of insomnia is very high (approximately 70%). This naturally gives rise to at least two questions: how will such symptomatology be addressed (within primary or specialty care) and what affect might targeted treatment for insomnia have on health were it a focus of treatment in general? Support (if any) Vargas: K23HL141581; Perlis: K24AG055602





2015 ◽  
Vol 34 (2) ◽  
pp. 63-72 ◽  
Author(s):  
Graham Gaylord ◽  
S. Kathleen Bailey ◽  
John M. Haggarty

This study describes a shared mental health care (SMHC) model introduced in Northern Ontario and examines how its introduction affected primary care provider (PCP) mental health referral patterns. A chart review examined referrals (N = 4,600) from 5 PCP sites to 5 outpatient community mental health services from January 2001 to December 2005. PCPs with access to SMHC made significantly more mental health referrals (p < 0.001). Two demographically similar PCPs were then compared, one co-located with SMHC. Referrals for depression to non-SMHC mental health services were 1.69 times more likely to be from the PCP not co-located with SMHC (p < 0.001). Findings suggest SMHC increases access to care and decreases demand on existing mental health services.



2015 ◽  
Vol 38 (2) ◽  
pp. 158-168 ◽  
Author(s):  
Wayne D. Bentham ◽  
Anna Ratzliff ◽  
David Harrison ◽  
Ya-Fen Chan ◽  
Steven Vannoy ◽  
...  


2005 ◽  
Vol 11 (3) ◽  
pp. 32 ◽  
Author(s):  
David Perkins ◽  
David Lyle

This paper reports on the evaluation of an Australian Government and NSW State funded Mental Health Integration Project in remote far western NSW. The project was part of the Mental Health Integration Program, developed from the Second National Mental Health Plan. The project implemented a model of community-based mental health services and used innovative financing arrangements to allow the provision of community-based specialist mental health teams to remote communities and to recruit visiting psychiatrists to support the local primary care providers. The evaluation strategy included a survey of general practitioners (GPs) in the Upper Western Sector and Broken Hill, designed to investigate their level and type of contact with psychiatrists and community-based specialist mental health care teams, their perceptions about the impact of the new services, and their interest in further professional development in mental health care.The project has shown that visiting specialists can be deployed in a primary care setting with a focus on meeting the needs of local GPs, primary health care staff and their patients.



2013 ◽  
Vol 178 (2) ◽  
pp. e248-e254 ◽  
Author(s):  
Robert L. Tong ◽  
Jason Lane ◽  
Patrick McCleskey ◽  
Brian Montenegro ◽  
Katherine Mansalis


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