Clinical trial NCT06263413: Clinical Workflow Optimization Using Artificial Intelligence for Dermatological Conditions
Official title: Optimization of Clinical Workflow in Patients With Various Dermatological Conditions Through Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Acne
Acne Vulgaris
Alopecia, Androgenetic
Pigmented Lesions
Pigmented Skin Lesion
Keywords: Acne
Androgenetic alopecia
Pigmented lesions
Artificial Intelligence
Algorithms
Classification
Detection
Lead sponsor: AI Labs Group S.L
Sponsor class: INDUSTRY
Collaborators: name: Instituto de Dermatología Integral (IDEI)
First posted: 2024-02-16
Enrollment: 150
Sex: ALL
Minimum age (years): 16
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with pigmented lesions and suspected malignancy, patients diagnosed with inflammatory acne and women with androgenetic alopecia. All these people will be treated at the dermatology service of IDEI hospitals

Brief summary: 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\&#39; ability to detect malignant or suspicious pigmented lesions.

Demonstrate that Legit.Health improves healthcare professionals\&#39; ability and precision in measuring the degree of involvement in patients with female androgenetic alopecia.

Demonstrate that Legit.Health improves healthcare professionals\&#39; 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\&#39;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.

Detailed description: Artificial intelligence (AI) based on images presents enormous potential for improving visual diagnostic accuracy in the medical field. During the COVID-19 pandemic, limited access to in-person healthcare services drove changes in medical care, accelerating the adoption of telemedicine. In this context, the use of AI for triage and decision support can 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 a dermatological center. Both triage of these patients, clinical evaluation, and their follow-up require in-person resources and the dedication of the specialist and staff. The use of tools like AI can benefit these professionals in reducing these processes and optimizing the workload.

Advances in image recognition and interpretation, as well as in artificial intelligence, have driven innovations in diagnosing various pathologies, including skin conditions. Computer-Aided Diagnosis (CAD) systems and other algorithm-based technologies have demonstrated their 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 uses artificial intelligence to optimize clinical flow and the care process of patients with skin conditions, will be evaluated. The purpose of this tool is the automatic prioritization of patients with greater urgency, assigning them the type of consultation (dermatological or aesthetic), improving the diagnostic capacity and detection of malignant pigmented lesions in auxiliary staff, as well as providing a visual record (photograph) of the condition for later review by external experts.

Thus, the main objective of this study is to validate that the Legit.Health tool, based on Artificial Intelligence, improves efficiency in clinical flow and the care process of patients, reducing the time and cost of care per patient through enhanced diagnostic accuracy and determination of the degree of malignancy or severity.

The secondary objectives focus on measuring the diagnostic performance of Legit.Health:

Demonstrate that Legit.Health improves the ability of healthcare professionals to detect malignant or suspicious pigmented lesions.

Demonstrate that Legit.Health improves the ability and precision of healthcare professionals in measuring the degree of involvement in patients with female androgenetic alopecia.

Demonstrate that Legit.Health improves the ability and precision of healthcare professionals 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 consulting for pigmented lesions.

Evaluate the reduction in healthcare resource usage by the center by reducing the number of triage consultations and directly referring the patient to the appropriate consultation (esthetic or dermatological).

Evaluate Legit.Health\&#39;s usability by the patient. Demonstrate that Legit.Health increases specialist satisfaction. Evaluate the reduction in healthcare resource usage by reducing the number of triage consultations and directly referring the patient 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.

Eligibility criteria: Inclusion Criteria:

* Patients aged 18 years or older (16 years for acne).
* Patients with pigmented lesions meeting any of the following conditions:
* Presenting for the first time with a pigmented lesion.
* Previously scheduled for dermoscopy consultation for the first time or for review of pigmented lesions.
* Patients with active inflammatory acne.
* Women with androgenetic alopecia.

Exclusion Criteria:

* Patients deemed by the investigator to be unable or unwilling to comply with the study procedures.

Primary outcomes:
measure: Concordance between the physician's diagnosis and that of the tool.
timeFrame: At the moment of enrollment up to 1 year
measure: Agreement of detected malignancy between the dermatologist and Legit.Health tool
timeFrame: At the moment of enrollment up to 1 year
Secondary outcomes:
measure: Acne severity
timeFrame: At the moment of enrollment up to 1 year
measure: Severity of alopecia
timeFrame: At the moment of enrollment up to 1 year
Other outcomes:
Locations:
city: Madrid
country: Spain
facility: IDEI Hospital
Study officials:
affiliation: IDEI Hospital
name: Miguel Sánchez-Viera, PhD
role: PRINCIPAL_INVESTIGATOR
References: