Ignite Creation Date:
2024-05-06 @ 8:02 PM
Last Modification Date:
2024-10-26 @ 3:18 PM
Study NCT ID:
NCT06221397
Status:
RECRUITING
Last Update Posted:
2024-01-25
First Post:
2024-01-15
Brief Title:
Clinical Validation Study of an AI-based CAD System for Early Non-Invasive Detection of Cutaneous Melanoma
Sponsor:
AI Labs Group SL
Organization:
AI Labs Group SL
Study Overview
Official Title:
Clinical Validation Study of a CAD System With Artificial Intelligence Algorithms for Early Non-invasive Detection of in Vivo Cutaneous Melanoma
Status:
RECRUITING
Status Verified Date:
2024-01
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
LEGIT_MC_EVCDA
Brief Summary:
The goal of this Cross-sectional analytical observational study of clinical case series is to validate a Computer-aided diagnosis software developed by AI Labs Group for the identification of cutaneous melanoma in images of lesions taken with a dermatoscopic camera This study will be carried out in patients with skin lesions with suspected malignancy seen at the Dermatology Department of the Cruces University Hospital and Basurto University Hospital The main questions it aims to answer are
If the AI algorithm developed by AI Labs group is a valid tool to identify cutaneous melanoma in dermoscopic images with high reliability
Comparing the device39s performance with dermatologists with primary care physicians39 assessment to be considered in later phases
Assessing the utility and feasibility of the device in adverse environments with technical limitations
In this way patients with skin lesions with suspected malignancy seen at the Dermatology Department of the Cruces and Basurto University Hospitals will be recruited Patients in this study will not receive any specific treatment as part of the research protocol In addition they will continue their regular prescribed medications and treatments as directed by their primary healthcare providers This study does not require doing a follow-up of the subjects Every patient only gets their skin lesions photographed at the time of visit
If the AI algorithm developed by AI Labs group is a valid tool to identify cutaneous melanoma in dermoscopic images with high reliability
Comparing the device39s performance with dermatologists with primary care physicians39 assessment to be considered in later phases
Assessing the utility and feasibility of the device in adverse environments with technical limitations
In this way patients with skin lesions with suspected malignancy seen at the Dermatology Department of the Cruces and Basurto University Hospitals will be recruited Patients in this study will not receive any specific treatment as part of the research protocol In addition they will continue their regular prescribed medications and treatments as directed by their primary healthcare providers This study does not require doing a follow-up of the subjects Every patient only gets their skin lesions photographed at the time of visit
Detailed Description:
Introduction Cutaneous melanoma CM a type of skin cancer has seen a significant rise in incidence and mortality It39s particularly aggressive and can metastasize rapidly making it resistant to chemotherapy and radiotherapy However early detection through simple surgical excision is highly treatable Differentiating between benign and malignant pigmented lesions especially during visual examination is challenging
Due to low public awareness and limited access to dermatologists melanoma often gets diagnosed at a later stage To address this there39s growing interest in computer-aided diagnostics CAD using Artificial intelligence AI for early melanoma detection AI technologies have shown competence comparable to dermatologists in classifying lesions from photographs Machine vision and AI present a significant opportunity for improving diagnosis
Preventive activities and early diagnosis campaigns have improved patient survival pointing at the fact that AI-based devices to assess skin lesion malignancy and distinguish between micro melanomas and other skin lesions like nevus and lentigines may further increase patient survival This study aims to clinically validate the detection of cutaneous melanoma using computer vision and machine learning applications
Objectives Hypothesis A CAD system powered by with machine vision allows early and non-invasive diagnosis of cutaneous melanoma in-vivo
Primary objective
To validate that the artificial intelligence algorithm developed by AI Labs Group SL for the identification of cutaneous melanoma in images of lesions taken with a dermatoscopic camera achieves the following values
Area Under the Curve AUC greater than 08
Sensitivity of 80 or higher
Specificity of 70 or higher
Secondary objective
To compare the performance of the artificial intelligence algorithm developed by the manufacturer with the performance of healthcare professionals of different specializations
Dermatologists Primary care physicians Validate the usefulness and feasibility of the artificial intelligence algorithm developed by the manufacturer in adverse environments with severe technical limitations such as lack of instrumentation or lack of internet connection
PRIMARY CARE PHYSICIANS The study does not compare the performance of the device against Primary care physicians it only focuses on dermatologists However it is widely known that dermatologists have a significantly higher diagnostic success rate in the detection of melanoma
Population Patients with skin lesions of suspected malignancy seen at the Dermatology Department of the Cruces and Basurto University Hospitals
Design and Methods Design This is an analytical observational case series study for the performance of a diagnostic test study Measurements are performed in a single case so it is a cross-sectional study
Number of Subjects The initial number of subjects for the study was 40 However due to the need for a balanced dataset ie same number of melanoma and non-melanoma images we considered it necessary to collect cases of nevus andor other types of skin lesions if necessary For this reason the proposed number of subjects was increased to approximately 200 people of which at least 40 present cutaneous melanoma
At the time of this report a total of 96 subjects have been included in the study 70 from Basurto University Hospital and 26 from Cruces University Hospital
Initiation Date The date of inclusion of the first subject was September 17th 2020
Completion Date The last subject of the initial sample of 40 participants was included on March 24 2021
The readjusted target sample size 200 participants has not been reached yet with 96 subjects included at the time of the report
Duration This study is estimated to have a recruitment period of 10 months for the inclusion of the first 40 patients The recruitment period is extended by 12 months for the inclusion of patients up to a total of 200 with a minimum of 40 melanomas
The total duration of the study is estimated at 36 months including the time required after recruitment of the last subject for closing and editing the database data analysis and preparation of the final study report
Methods
All the skin lesions are photographed following these technical indications
Uncompressed image format such as PNG HEIC or TIFF Taken with the DermLite Foto X dermatoscope of the 3Gen Inc
Taken from a Smartphone with the following characteristics
With a camera with a minimum resolution of not less than 13 megapixels
Taken with one of the following models
Google Pixel 3 and Google Pixel 3 XL
Samsung Galaxy Note 10 Samsung Galaxy S10 Samsung Galaxy S10E
iPhone X and below
Disabling all image post-processing such as HDR portrait mode color filters or digital zoom
On a monthly basis the research team collects the images and verifies their correctness If any image is not of sufficient quality the investigator repeats the photograph The research team also collects diagnostic data from the expert dermatologists
Due to low public awareness and limited access to dermatologists melanoma often gets diagnosed at a later stage To address this there39s growing interest in computer-aided diagnostics CAD using Artificial intelligence AI for early melanoma detection AI technologies have shown competence comparable to dermatologists in classifying lesions from photographs Machine vision and AI present a significant opportunity for improving diagnosis
Preventive activities and early diagnosis campaigns have improved patient survival pointing at the fact that AI-based devices to assess skin lesion malignancy and distinguish between micro melanomas and other skin lesions like nevus and lentigines may further increase patient survival This study aims to clinically validate the detection of cutaneous melanoma using computer vision and machine learning applications
Objectives Hypothesis A CAD system powered by with machine vision allows early and non-invasive diagnosis of cutaneous melanoma in-vivo
Primary objective
To validate that the artificial intelligence algorithm developed by AI Labs Group SL for the identification of cutaneous melanoma in images of lesions taken with a dermatoscopic camera achieves the following values
Area Under the Curve AUC greater than 08
Sensitivity of 80 or higher
Specificity of 70 or higher
Secondary objective
To compare the performance of the artificial intelligence algorithm developed by the manufacturer with the performance of healthcare professionals of different specializations
Dermatologists Primary care physicians Validate the usefulness and feasibility of the artificial intelligence algorithm developed by the manufacturer in adverse environments with severe technical limitations such as lack of instrumentation or lack of internet connection
PRIMARY CARE PHYSICIANS The study does not compare the performance of the device against Primary care physicians it only focuses on dermatologists However it is widely known that dermatologists have a significantly higher diagnostic success rate in the detection of melanoma
Population Patients with skin lesions of suspected malignancy seen at the Dermatology Department of the Cruces and Basurto University Hospitals
Design and Methods Design This is an analytical observational case series study for the performance of a diagnostic test study Measurements are performed in a single case so it is a cross-sectional study
Number of Subjects The initial number of subjects for the study was 40 However due to the need for a balanced dataset ie same number of melanoma and non-melanoma images we considered it necessary to collect cases of nevus andor other types of skin lesions if necessary For this reason the proposed number of subjects was increased to approximately 200 people of which at least 40 present cutaneous melanoma
At the time of this report a total of 96 subjects have been included in the study 70 from Basurto University Hospital and 26 from Cruces University Hospital
Initiation Date The date of inclusion of the first subject was September 17th 2020
Completion Date The last subject of the initial sample of 40 participants was included on March 24 2021
The readjusted target sample size 200 participants has not been reached yet with 96 subjects included at the time of the report
Duration This study is estimated to have a recruitment period of 10 months for the inclusion of the first 40 patients The recruitment period is extended by 12 months for the inclusion of patients up to a total of 200 with a minimum of 40 melanomas
The total duration of the study is estimated at 36 months including the time required after recruitment of the last subject for closing and editing the database data analysis and preparation of the final study report
Methods
All the skin lesions are photographed following these technical indications
Uncompressed image format such as PNG HEIC or TIFF Taken with the DermLite Foto X dermatoscope of the 3Gen Inc
Taken from a Smartphone with the following characteristics
With a camera with a minimum resolution of not less than 13 megapixels
Taken with one of the following models
Google Pixel 3 and Google Pixel 3 XL
Samsung Galaxy Note 10 Samsung Galaxy S10 Samsung Galaxy S10E
iPhone X and below
Disabling all image post-processing such as HDR portrait mode color filters or digital zoom
On a monthly basis the research team collects the images and verifies their correctness If any image is not of sufficient quality the investigator repeats the photograph The research team also collects diagnostic data from the expert dermatologists
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None