An observational study in Non-melanoma Skin Cancer, sponsored by Skin Analytics Limited. Completed at 3 sites in United Kingdom. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2022-05-18.
Sponsored by Skin Analytics Limited · Observational
This study aims to establish the effectiveness of an Artificial Intelligence (AI) algorithm (DERM) to determine the presence of Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC) and frequently observed benign conditions, when used to analyse images of skin lesions taken by commonly available smart phone cameras.
DERM, an Artificial Intelligence (AI)-based diagnosis support tool, has been shown to be able to accurately identify Non-melanoma skin cancers (NMSC) and other conditions from historical images of suspicious skin lesions (moles). This study aims to establish how well DERM determines the presence of these conditions in images of skin lesions collected in a clinical setting.
Suspicious skin lesions that are due to be assessed by a dermatologist and a patch of healthy skin will be photographed using three commonly available smart phone cameras with a specific lens attachment. The images will be analysed by DERM, and the results compared to the clinician's diagnosis (all lesions) and histologically-conformed diagnosis (any lesion that is biopsied).
582 studies on the registry are indexed under Skin Neoplasms; 114 are open to participants now.
This study's enrollment of 572 is above the median of 200 across 134 observational studies indexed under Skin Neoplasms.
Browse Skin Neoplasms studies →Skin Analytics Limited is the lead sponsor of 5 studies on the registry; none are open to participants now.
Counted across the registry records on this site, refreshed daily.
Patients attending a dermatology clinic with at least 1 suspicious skin lesion
Exclusion Criteria:
Recruited participants will be attending a dermatology clinic with at least one skin lesion where there is a suspicion of skin cancer. All suspicious lesions suitable for photographing will be photographed six times in a single visit. A macro and dermoscopic image of each lesion will be captured by three different mobile phones: an iPhone, a Samsung and a Nokia smart phone, without (macro image) or with (dermoscopic image) a Dermlite DL1 lens attached. Dermoscopic images of healthy skin will also be captured by each camera. Images of the lesions will be analysed by DERM. The DERM results for lesions biopsied will be compared to the biopsy result; the DERM results for lesions not biopsied will be compared to the clinical assessment.
Device: Deep Ensemble for the Recognition of Malignancy (DERM)
An AI-based diagnosis support tool
AUROC of DERM performance when analysing images of biopsied lesions
Area Under the Receiver Operating Characteristic Curve (AUROC) of the DERM result of biopsied lesions, using histopathological-confirmed diagnosis as gold standard
Time frame: Study completion
AUROC of DERM performance when analysing images of non-biopsied lesions
Area Under the Receiver Operating Characteristic Curve (AUROC) of the DERM result of biopsied lesions, using clinical diagnosis as gold standard
Time frame: Study completion: on average 2 days
The sensitivity of DERM when used to assess biopsied lesions
The sensitivity of DERM when used to assess biopsied lesions
Time frame: Study completion: on average 2 days
The specificity of DERM when used to assess biopsied lesions
The specificity of DERM when used to assess biopsied lesions
Time frame: Study completion: on average 2 days
The false positive rate of DERM when used to assess biopsied lesions
The false positive rate of DERM when used to assess biopsied lesions
Time frame: Study completion: on average 2 days
The false negative rate of DERM when used to assess biopsied lesions
The false negative rate of DERM when used to assess biopsied lesions
Time frame: Study completion: on average 2 days
The positive predictive value of DERM when used to assess biopsied lesions
The positive predictive value of DERM when used to assess biopsied lesions
Time frame: Study completion: on average 2 days
The negative predictive value of DERM when used to assess biopsied lesions
The negative predictive value of DERM when used to assess biopsied lesions
Time frame: Study completion: on average 2 days
The sensitivity of DERM when used to assess non-biopsied lesions
The sensitivity of DERM when used to assess non-biopsied lesions
Time frame: Study completion: on average 2 days
The specificity of DERM when used to assess non-biopsied lesions
The specificity of DERM when used to assess non-biopsied lesions
Time frame: Study completion: on average 2 days
The false positive rate of DERM when used to assess non-biopsied lesions
The false positive rate of DERM when used to assess non-biopsied lesions
Time frame: Study completion: on average 2 days
The false negative rate of DERM when used to assess non-biopsied lesions
The false negative rate of DERM when used to assess non-biopsied lesions
Time frame: Study completion: on average 2 days
The positive predictive value of DERM when used to assess non-biopsied lesions
The positive predictive value of DERM when used to assess non-biopsied lesions
Time frame: Study completion: on average 2 days
The negative predictive value of DERM when used to assess non-biopsied lesions
The negative predictive value of DERM when used to assess non-biopsied lesions
Time frame: Study completion: on average 2 days
Concordance of clinician assessment with histologically confirmed diagnosis
Concordance of clinician assessment with histologically confirmed diagnosis
Time frame: Study completion: on average 2 days
The concordance of DERM result generated using images from each camera
The concordance of DERM result generated using images from each camera
Time frame: Study completion: on average 2 days
The proportion of skin lesions with 3 images that can be analysed by DERM;
The proportion of skin lesions with 3 images that can be analysed by DERM;
Time frame: Study completion: on average 2 days
The proportion of skin lesions with at least 1 readable image that can be analysed by DERM
The proportion of skin lesions with at least 1 readable image that can be analysed by DERM
Time frame: Study completion: on average 2 days
Impact of patient characteristics on the DERM and clinician assessment
The impact of patient characteristics (such as sex, age, location of lesion, total body lesion count, Fitzpatrick skin type, past medical history of skin cancer) on the diagnostic accuracy of DERM and clinician assessment;
Time frame: Study completion: on average 2 days
Impact of lesion characteristics on the DERM and clinician assessment
The impact of lesions characteristic (such as growth over last 6 months, stage and sub-type) on the diagnostic accuracy of DERM and clinician assessment
Time frame: Study completion: on average 2 days
The impact of image variables on the diagnostic accuracy of DERM assessment
The impact of image variables (such as macro and dermoscopic images) on the diagnostic accuracy of DERM assessment
Time frame: Study completion: on average 2 days
DERM performance (AUROC) when macro images are used both to train the algorithm and as test images
Exploration of whether macro images can be used as part of DERM's assessment
Time frame: Study completion: on average 2 days
Plan to share: Undecided — Research to improve or test the performance of DERM only allowed in consent
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This study is completed, as verified in May 2022. You cannot join it, but the record below documents what was studied.
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Skin Analytics Limited