Nctid:
NCT06221397
Payload:
{"hasResults"=>false, "derivedSection"=>{"miscInfoModule"=>{"versionHolder"=>"2024-10-04"}, "conditionBrowseModule"=>{"meshes"=>[{"id"=>"D000008545", "term"=>"Melanoma"}, {"id"=>"D000096142", "term"=>"Melanoma, Cutaneous Malignant"}], "ancestors"=>[{"id"=>"D000018358", "term"=>"Neuroendocrine Tumors"}, {"id"=>"D000017599", "term"=>"Neuroectodermal Tumors"}, {"id"=>"D000009373", "term"=>"Neoplasms, Germ Cell and Embryonal"}, {"id"=>"D000009370", "term"=>"Neoplasms by Histologic Type"}, {"id"=>"D000009369", "term"=>"Neoplasms"}, {"id"=>"D000009380", "term"=>"Neoplasms, Nerve Tissue"}, {"id"=>"D000018326", "term"=>"Nevi and Melanomas"}, {"id"=>"D000012878", "term"=>"Skin Neoplasms"}, {"id"=>"D000009371", "term"=>"Neoplasms by Site"}, {"id"=>"D000012871", "term"=>"Skin Diseases"}], "browseLeaves"=>[{"id"=>"M11528", "name"=>"Melanoma", "asFound"=>"Melanoma", "relevance"=>"HIGH"}, {"id"=>"M3212", "name"=>"Melanoma, Cutaneous Malignant", "asFound"=>"Cutaneous Melanoma", "relevance"=>"HIGH"}, {"id"=>"M20495", "name"=>"Neuroendocrine Tumors", "relevance"=>"LOW"}, {"id"=>"M19845", "name"=>"Neuroectodermal Tumors", "relevance"=>"LOW"}, {"id"=>"M20388", "name"=>"Neuroectodermal Tumors, Primitive", "relevance"=>"LOW"}, {"id"=>"M12318", "name"=>"Neoplasms, Germ Cell and Embryonal", "relevance"=>"LOW"}, {"id"=>"M12315", "name"=>"Neoplasms by Histologic Type", "relevance"=>"LOW"}, {"id"=>"M12325", "name"=>"Neoplasms, Nerve Tissue", "relevance"=>"LOW"}, {"id"=>"M20470", "name"=>"Nevi and Melanomas", "relevance"=>"LOW"}, {"id"=>"M12446", "name"=>"Nevus", "relevance"=>"LOW"}, {"id"=>"M12448", "name"=>"Nevus, Pigmented", "relevance"=>"LOW"}, {"id"=>"M15681", "name"=>"Skin Neoplasms", "relevance"=>"LOW"}, {"id"=>"M15674", "name"=>"Skin Diseases", "relevance"=>"LOW"}, {"id"=>"T4091", "name"=>"Neuroendocrine Tumor", "relevance"=>"LOW"}, {"id"=>"T4092", "name"=>"Neuroepithelioma", "relevance"=>"LOW"}], "browseBranches"=>[{"name"=>"Neoplasms", "abbrev"=>"BC04"}, {"name"=>"Skin and Connective Tissue Diseases", "abbrev"=>"BC17"}, {"name"=>"All Conditions", "abbrev"=>"All"}, {"name"=>"Rare Diseases", "abbrev"=>"Rare"}]}}, "protocolSection"=>{"designModule"=>{"studyType"=>"OBSERVATIONAL", "designInfo"=>{"timePerspective"=>"CROSS_SECTIONAL", "observationalModel"=>"COHORT"}, "enrollmentInfo"=>{"type"=>"ESTIMATED", "count"=>200}, "targetDuration"=>"1 Day", "patientRegistry"=>true}, "statusModule"=>{"overallStatus"=>"RECRUITING", "startDateStruct"=>{"date"=>"2020-09-17", "type"=>"ACTUAL"}, "expandedAccessInfo"=>{"hasExpandedAccess"=>false}, "statusVerifiedDate"=>"2024-01", "completionDateStruct"=>{"date"=>"2024-09", "type"=>"ESTIMATED"}, "lastUpdateSubmitDate"=>"2024-01-24", "studyFirstSubmitDate"=>"2024-01-15", "studyFirstSubmitQcDate"=>"2024-01-23", "lastUpdatePostDateStruct"=>{"date"=>"2024-01-25", "type"=>"ACTUAL"}, "studyFirstPostDateStruct"=>{"date"=>"2024-01-24", "type"=>"ACTUAL"}, "primaryCompletionDateStruct"=>{"date"=>"2024-09", "type"=>"ESTIMATED"}}, "outcomesModule"=>{"primaryOutcomes"=>[{"measure"=>"Number of images per subject", "timeFrame"=>"The moment of enrollment", "description"=>"Number of images that every patient upload to the web app with tha AI algorithm"}, {"measure"=>"Diagnosis", "timeFrame"=>"Moment of enrollment", "description"=>"Diagnosis made by the AI algorithm"}, {"measure"=>"Quality of the image", "timeFrame"=>"The moment of enrollment", "description"=>"Quality of the uploaded image by patient. A bad quality will not be accepted for the study and the patient will be asked to upload an image with a better quality"}, {"measure"=>"Melanoma detection", "timeFrame"=>"The moment of enrollment", "description"=>"Sensitivity of the algorithm to detect the presence of melanoma or not"}], "secondaryOutcomes"=>[{"measure"=>"Age and sex", "timeFrame"=>"The moment of enrollment", "description"=>"Age and sex of patients"}]}, "oversightModule"=>{"oversightHasDmc"=>false, "isFdaRegulatedDrug"=>false, "isFdaRegulatedDevice"=>false}, "conditionsModule"=>{"keywords"=>["melanoma", "primary care", "dermatology", "diagnosis", "Artificial Intelligence", "Severity"], "conditions"=>["Melanoma", "Melanoma, Skin"]}, "descriptionModule"=>{"briefSummary"=>"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:\n\n* If the AI algorithm developed by AI Labs group is a valid tool to identify cutaneous melanoma in dermoscopic images with high reliability.\n* Comparing the device\\'s performance with dermatologists, with primary care physicians\\' assessment to be considered in later phases.\n* Assessing the utility and feasibility of the device in adverse environments with technical limitations.\n\nIn 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.", "detailedDescription"=>"Introduction Cutaneous melanoma (CM), a type of skin cancer, has seen a significant rise in incidence and mortality. It\\'s 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.\n\nDue to low public awareness and limited access to dermatologists, melanoma often gets diagnosed at a later stage. To address this, there\\'s 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.\n\nPreventive 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.\n\nObjectives Hypothesis A CAD system powered by with machine vision allows early and non-invasive diagnosis of cutaneous melanoma in-vivo.\n\nPrimary objective\n\nTo validate that the artificial intelligence algorithm developed by AI Labs Group S.L. for the identification of cutaneous melanoma in images of lesions taken with a dermatoscopic camera achieves the following values:\n\n* Area Under the Curve (AUC) greater than 0.8\n* Sensitivity of 80% or higher\n* Specificity of 70% or higher\n\nSecondary objective\n\nTo compare the performance of the artificial intelligence algorithm developed by the manufacturer with the performance of healthcare professionals of different specializations:\n\nDermatologists 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.\n\nPRIMARY 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.\n\nPopulation Patients with skin lesions of suspected malignancy seen at the Dermatology Department of the Cruces and Basurto University Hospitals.\n\nDesign 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.\n\nNumber of Subjects The initial number of subjects for the study was 40. However, due to the need for a balanced dataset (i.e., same number of melanoma and non-melanoma images), we considered it necessary to collect cases of nevus and/or 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.\n\nAt 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.\n\nInitiation Date The date of inclusion of the first subject was September 17th, 2020.\n\nCompletion Date The last subject of the initial sample of 40 participants was included on March 24, 2021.\n\nThe readjusted target sample size (200 participants) has not been reached yet, with 96 subjects included at the time of the report.\n\nDuration 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.\n\nThe 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.\n\nMethods\n\nAll the skin lesions are photographed following these technical indications:\n\nUncompressed image format, such as PNG, HEIC or TIFF. Taken with the DermLite Foto X dermatoscope of the 3Gen Inc.\n\nTaken from a Smartphone with the following characteristics:\n\nWith a camera with a minimum resolution of not less than 13 megapixels.\n\nTaken with one of the following models:\n\n* Google Pixel 3 and Google Pixel 3 XL.\n* Samsung Galaxy Note 10, Samsung Galaxy S10, Samsung Galaxy S10E\n* iPhone X and below\n* Disabling all image post-processing, such as HDR, portrait mode, color filters or digital zoom.\n\nOn 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."}, "eligibilityModule"=>{"sex"=>"ALL", "stdAges"=>["ADULT", "OLDER_ADULT"], "minimumAge"=>"18 years", "samplingMethod"=>"NON_PROBABILITY_SAMPLE", "studyPopulation"=>"Patients with skin lesions with suspected malignancy seen at the Dermatology Department of the Hospital Universitario Cruces and Hospital Universitario Basurto.", "healthyVolunteers"=>false, "eligibilityCriteria"=>"Inclusion Criteria:\n\n* Patients with skin lesions with suspected malignancy\n* Age over 18 years old\n* Patients who consent to participate in the study by signing the Informed Consent form\n\nExclusion Criteria:\n\n* Patients under 18 years of age"}, "identificationModule"=>{"nctId"=>"NCT06221397", "acronym"=>"LEGIT_MC_EVCDA", "briefTitle"=>"Clinical Validation Study of an AI-based CAD System for Early Non-Invasive Detection of Cutaneous Melanoma", "organization"=>{"class"=>"INDUSTRY", "fullName"=>"AI Labs Group S.L"}, "officialTitle"=>"Clinical Validation Study of a CAD System With Artificial Intelligence Algorithms for Early Non-invasive Detection of in Vivo Cutaneous Melanoma", "orgStudyIdInfo"=>{"id"=>"PI2019216"}}, "contactsLocationsModule"=>{"locations"=>[{"zip"=>"48903", "city"=>"Barakaldo", "state"=>"Biscay", "status"=>"RECRUITING", "country"=>"Spain", "contacts"=>[{"name"=>"Jesús Gardeazabal, PhD", "role"=>"CONTACT", "email"=>"secretaria.dermatologiacruces@osakidetza.net", "phone"=>"+34 946006149"}, {"name"=>"Alfonso Medela, MsC", "role"=>"CONTACT", "email"=>"alfonso@legit.health", "phone"=>"+34 638127476"}, {"name"=>"Jesús Gardeazabal, PhD", "role"=>"PRINCIPAL_INVESTIGATOR"}, {"name"=>"Rosa María Izu, PhD", "role"=>"PRINCIPAL_INVESTIGATOR"}, {"name"=>"Juan Antonio Ratón Nieto, PhD", "role"=>"SUB_INVESTIGATOR"}, {"name"=>"Ana Sánchez Diez, PhD", "role"=>"SUB_INVESTIGATOR"}, {"name"=>"Alfonso Medela, MsC", "role"=>"SUB_INVESTIGATOR"}, {"name"=>"Andy Aguilar, MsC", "role"=>"SUB_INVESTIGATOR"}, {"name"=>"Taig Mac Carthy, MsC", "role"=>"SUB_INVESTIGATOR"}], "facility"=>"University Hospital of Cruces", "geoPoint"=>{"lat"=>43.29639, "lon"=>-2.98813}}], "centralContacts"=>[{"name"=>"Alfonso Medela, MsC", "role"=>"CONTACT", "email"=>"alfonso@legit.health", "phone"=>"+34 638127476"}, {"name"=>"Jordi Barrachina, PhD", "role"=>"CONTACT", "email"=>"jordibarrachina@legit.health", "phone"=>"+34 660675578"}], "overallOfficials"=>[{"name"=>"Jesús Gardeazabal, PhD", "role"=>"PRINCIPAL_INVESTIGATOR", "affiliation"=>"Hospital Universitario Cruces"}, {"name"=>"Rosa María Ize, PhD", "role"=>"PRINCIPAL_INVESTIGATOR", "affiliation"=>"Hospital Universitario Basurto"}]}, "sponsorCollaboratorsModule"=>{"leadSponsor"=>{"name"=>"AI Labs Group S.L", "class"=>"INDUSTRY"}, "collaborators"=>[{"name"=>"Servicio Vasco de Salud Osakidetza, Spain", "class"=>"UNKNOWN"}, {"name"=>"Osakidetza", "class"=>"OTHER"}, {"name"=>"Hospital de Basurto", "class"=>"OTHER"}, {"name"=>"Hospital de Cruces", "class"=>"OTHER"}], "responsibleParty"=>{"type"=>"SPONSOR"}}}}
