Pilot Study: AI Algorithm for Dermatology Referral Optimization — DAO_DERIVATION  

Skin Diseases Clinical Trial

Official title: Pilot Study for the Clinical Validation of an Artificial Intelligence Algorithm to Optimize the Appropriateness of Dermatology Referrals.

Status Recruiting

Clinical Trial Summary

The goal of this observational prospective and analytical study is to validate that the device is a valid tool for improving the adequacy of referrals to dermatology. As a secondary objectives the investigators set out to validate that the device reduces costs in secondary care; to validate that the device reduces dermatology waiting lists and to validate that the device optimizes clinical flow in Osakidetza. The primary care physician will explain to the patient what his/her participation in the study will consist of by means of the Patient Information Sheet. The patient, in turn, will be able to ask all the questions he/she considers appropriate in order to clarify all his/her doubts regarding the study. If the patient wishes to participate in the study, he/she will sign the Informed Consent Form and will be assigned a study code. After signing the informed consent, the data collection process begins. The Principal Investigator and/or collaborating investigators assigned to this task will collect demographic data (age, sex) and data related to the diagnosis, characteristics and treatment of the pathology. Primary care physicians should take photographs showing the areas affected by the pathology. These photographs will be taken with their own smartphone or using a mobile dermatoscope if the use of a mobile dermatoscope is clinically relevant. The primary care physician, will record the photographs periodically, uploading the images to a Google Drive folder that the study sponsor will enable at the beginning of the study. The photographs are named using a code that includes the patient ID (NNN) and photograph number (nn). Primary care physicians will assess the patient's pathology as they would in a routine consultation and record their diagnosis and referral criteria, and associate them with the patient's photographs and demographic data to be collected by the research team telematically at the end of the study duration. These information transfers and the storage of the photographs will be in line with the European Regulation 2016/679 of 27 April on the protection of natural persons with regard to the processing of personal data and the free movement of such data and the Organic Law 3/2018 of 5 December on the Protection of Personal Data and guarantee of digital rights. The specialist physicians will have a period of one month after the end of the recruitment period to evaluate and label the photographs taken. In this labeling process they will record their diagnosis, and whether they consider that the referral has been appropriate or not. This information will be collected by the research team telematically at the end of the study duration. This transfer of information and storage of the photographs will comply with European Regulation 2016/679, of 27 April, on the protection of natural persons with regard to the processing of personal data and the free movement of such data and with Organic Law 3/2018, of 5 December, on the Protection of Personal Data and guarantee of digital rights.

Clinical Trial Description

Skin-related diseases are a frequent reason for consultation in primary care; some studies quantify it at approximately 5% of all consultations made, mainly by the working population. This represents a considerable consumption of resources and makes an efficient approach to these conditions a key step in optimizing the performance of primary care. Many studies show discrepancies in opinion between the opinions of primary care physicians and dermatologists, with percentages of agreement in their diagnoses ranging from 57% to 65.52% depending on the study. In general, primary care physicians do not demonstrate adequate knowledge of skin diseases, their diagnosis and treatments. This human limitation when evaluating skin diseases is also reflected in the effort and time required to estimate the degree of involvement of a patient or the stage of the pathology. So much so, that it ends up being a very unrewarding task and can lead to poor adherence to the protocol and inadequate referrals. Time consumption is of particular concern given that the number of medical professionals, especially in dermatology, is not sufficient in relation to the demand that exists. Access of the general population to a dermatology specialist is complicated, due to the low number (3 dermatologists per 100,000 inhabitants), making it even more difficult in small population centers. Because of this, screening for dermatologic lesions should be performed by the primary care physician, whose diagnostic capacity is even lower and can increase the risk of misdiagnosis. In this regard, the literature shows a discordance of 55% to 65% between the primary care physician and the specialist6 and studies confirm a number of expected features: common dermatological diseases are often unrecognized or misdiagnosed by non-dermatologists, due to the particular profiles of common diagnoses in this activity (drug-induced rash, fungal infections). And in addition to these inherent limitations, in cases where the preliminary examination is performed by the patient, the possibility of bias is added. This is especially true in cases where the patient knows that the treatment he or she receives will be determined by the information he or she provides. In addition, the medical team lacks the means to ensure that the values reported by the patient are true, which precludes external verification. Fortunately, in recent years there has been an increasing demand to develop Computer Aided Diagnosis (CAD) systems and other systems that facilitate the detection of different pathologies through algorithms. CAD systems are an interdisciplinary technology that combines artificial intelligence and digital image processing. Image processing based on complex pattern recognition systems makes it possible for the physician to interpret the information contained in the medical image with much less difficulty. Advances in image recognition and artificial intelligence have led to innovations in the diagnosis of all types of pathologies. It has been demonstrated that through artificial intelligence (AI) algorithms it is possible to classify photographs of lesions with a level of competence comparable to that of a medical expert. Therefore, the use of artificial vision applications when gathering information about the patient's condition presents a huge advance that not only brings reliability to the documentation process, but also allows greater precision when measuring visual signs of the pathology, and consequently, informs the criteria for referral to the specialist. Consequently, this study aims to clinically validate a novel artificial intelligence tool for activity grading in affected patients. This innovation has the potential to facilitate medical practice in the diagnosis of skin cancer and improve the quality of life of patients affected by this pathology. In addition, this technology provides a new measurement tool that opens the door to a new field of research into the efficacy of treatments or the analysis of the pathology itself and its subtypes. The hypothesis guiding this study is that artificial intelligence algorithms developed by AI LABS GROUP SL significantly optimize the appropriateness of dermatology referrals. The main objective of this study is to validate that the artificial intelligence algorithms developed by AI LABS GROUP SL optimize the appropriateness of referrals to dermatology. That is, to reduce the number of mild or benign cases that are referred to the dermatologist, since they can be managed from primary care. This is an analytical prospective observational study of a series of clinical cases. It is a longitudinal study. This study estimates a recruitment period of 2 months. The specialist physicians will have 1 month to label the photographs. The investigators will have 1 month to close and edit the database, analyze the data, and prepare the final report of the study. The total duration of the study is estimated at 4 months. "proof-of-concept" pilot study in which the sample size has been estimated based on the number of patients with a diagnosis of skin diseases that can be seen in four primary care services of Hospital Universitario de Cruces. During the recruitment period of the study, all patients with a diagnosis of skin diseases who meet the selection criteria will be included. The data collected from these patients during the study period will be analyzed, and depending on the results obtained, it will be assessed whether it is necessary to expand the sample size to include more patients. The main variable aims to determine the efficiency of the algorithm in optimizing the appropriateness of dermatology referrals. To this end, the researchers will identify inappropriate referrals. The investigators define an inappropriate referral as one that, according to the criteria of the specialists consulted in the study, does not require the attention of a dermatologist in order to be treated, as may be the case of seborrheic keratosis. For this purpose, a photograph will be taken and the date of the patient's visit to the clinic and the criteria for moving to the next stage of the care process will be recorded. That is to say: the date of the visit attended by the primary care physician and whether or not it resulted in a referral to a dermatologist, as well as the referral criterion used, will be recorded. In addition, a photograph of the area affected by the pathology will be taken at this step. Later, the Principal Investigators will evaluate the photos and determine whether the referral was appropriate or not. ;

Related Conditions & MeSH terms

• Skin Condition
• Skin Diseases

• NCT number: NCT06228014
• Study type: Observational
• Source: AI Labs Group S.L
• Contact: Alfonso Medela, MsC
• Phone: +34 638127476
• Email: alfonso@legit.health
• Status: Recruiting
• Start date: April 7, 2022
• Completion date: December 30, 2024