View clinical trials related to Pigmented Skin Lesion.
Filter by:Artificial intelligence (AI) based on imaging holds tremendous potential to enhance visual diagnostic accuracy in the medical field. Amid the COVID-19 pandemic, limited access to in-person healthcare services drove shifts in medical care, hastening the adoption of telemedicine. In this context, AI usage for triage and decision support may be crucial for professionals to manage workload and improve performance. In dermatology, pigmented lesions, acne, and alopecia are three recurring pathology groups with high demand in dermatological centers. Both triage, clinical evaluation, and patient follow-up require in-person resources and specialist dedication. Employing tools like AI can benefit these professionals in reducing such processes and optimizing workload. Advancements in image recognition and interpretation, as well as in artificial intelligence, have spurred innovations in diagnosing various pathologies, including skin conditions. Computer-Aided Diagnosis (CAD) systems and other algorithm-based technologies have demonstrated the ability to classify lesion images with a competency comparable to that of an expert physician. In this study, the Legit.Health tool, developed by AI LABS GROUP S.L., which utilizes artificial intelligence to optimize clinical flow and patient care processes for skin conditions, will be evaluated. The purpose of this tool is to automatically prioritize patients with greater urgency, assign the type of consultation (dermatological or aesthetic), enhance diagnostic capability and detection of malignant pigmented lesions in auxiliary staff, and provide a visual record (photograph) of the condition for later review by external experts. Thus, the main objective of this study is to validate that Legit.Health, based on Artificial Intelligence, improves efficiency in clinical flow and patient care processes, thereby reducing time and cost of patient care through enhanced diagnostic accuracy and severity determination. The secondary objectives focus on measuring the diagnostic performance of Legit.Health: Demonstrate that Legit.Health enhances healthcare professionals' ability to detect malignant or suspicious pigmented lesions. Demonstrate that Legit.Health improves healthcare professionals' ability and precision in measuring the degree of involvement in patients with female androgenetic alopecia. Demonstrate that Legit.Health improves healthcare professionals' ability and precision in measuring the degree of involvement in patients with acne. Additionally, the study aims to assess the utility of this tool: Automate the triage/initial assessment process in patients presenting with pigmented lesions. Evaluate the reduction in healthcare resources usage by the center by reducing the number of triage consultations and directing the patient directly to the appropriate consultation (esthetic or dermatological). Evaluate Legit.Health's usability by the patient. Demonstrate that Legit.Health increases specialist satisfaction. Evaluate the reduction in healthcare resources usage by reducing the number of triage consultations and directing the patient directly to the appropriate consultation, whether in aesthetic or dermatological settings. Methodology Study Design Type This is an observational study, both prospective with a longitudinal character and retrospective case series. Study Period This study estimates a recruitment period of 3 months. The total study duration is estimated at 6 months, including the previous time for retrospective analysis and the necessary time after recruiting the last subject for database closure and editing, data analysis, and preparation of the final study report. The total study duration for each participant with pigmented lesions will be 1-3 months. The duration for patients with acne and alopecia will be 1 day. Study Population Adult patients (≥ 18 years) with skin pathologies treated at the Dermatology Unit of IDEI.
The aim of this study is to evaluate the imaging performance of Spectrally Encoded Confocal Microscopy (SECM) for imaging human skin and skin diseases.
This study is being conducted to evaluate the safety and efficacy of a fractional diode laser for treatment of pigmented lesions such as, but not limited to lentigos (age spots), solar lentigos (sunspots), and ephelides (freckles).
Malignant melanoma (MM) is a deadly cancer, claiming globally about 160000 new cases per year and 48000 deaths at a 1:28 lifetime incidence (2016). The golden standard, dermoscopy, enables Dermatologists to diagnose with a sensitivity of 40%, and a 8-12% specificity, approximately. Additional diagnostic abilities are restricted to devices which are either unproved or experimental. A new technology of Neuronal Network Clinical Decision Support (NNCD) was developed. It uses a dermoscopic imaging device and a camera able to capture an image. The photo is transferred to a Cloud Server and further analyzed by a trained classifier. Classifier training is aimed at a high accuracy diagnosis of Dysplastic Nevi (DN), Spitz Nevi and Malignant Melanoma detection with assistance from a Deep Neuronal Learning network (DLN). Diagnosis output is an excise or do not excise recommendation for pigmented skin lesions. A total of 80 subjects already referred to biopsy pigmented skin lesions will be examined by dermoscopy imaging in a non interventional study. Artificial Intelligence output results, as measured by 2 different dermoscopes, to be compared to ground truth biopsies, by either classifier decisions or a novel Modified Classifier Technology output decisions. Primary endpoints are sensitivity and specificity detection of the NNCD techniques. Secondary endpoints are the positive and negative prediction ratios of NNCD techniques.
Study to evaluate the ability of the Dermal Cooling System to reduce pigmentation in benign pigmented lesions.
The investigators propose the use of functional photoacoustic microscopy (fPAM) to evaluate both benign and malignant pigmented lesions for tumor depth. Through fPAM analysis followed by histological examination, the investigators anticipate that they will be able to non-invasively determine tumor depth of pigmented lesions (moles and melanoma). In melanoma, tumor depth (Breslow's depth) is not only an important prognostic indicator, but also directs surgical treatment. The ultimate goal is to develop a sensitive clinical tool that will allow non-surgical evaluation of pigmented lesions, which eventually, will aid in melanoma diagnosis and management - potentially an earlier and more definitive surgical management. In addition, the investigators propose to use the combination of fPAM and single-cell PAM to respectively image CTCs in trunk vessels and cuticle capillaries. Based on the investigators' murine models, the investigators anticipate that they will be able to differentiate CTCs from other blood cells and reliably calculate CTC concentration in a non-invasive manner. CTC concentration has been demonstrated to be a valuable indicator of a melanoma's metastatic potential and a potential tool in evaluating treatment efficacy. The ultimate goal is to develop a sensitive imaging device that will allow accurate evaluation of the risk of melanoma recurrence and metastases, that may facilitate treatment monitoring.