The iToBoS project (carried out from April 2021 to March 2025) represents a groundbreaking achievement in the field of dermatology.
By focusing on early detection and personalized melanoma risk assessment, the project has successfully integrated advanced technologies, artificial intelligence, and multidisciplinary approaches to deliver an innovative diagnostic platform. This platform addresses critical challenges in melanoma detection with the aim of improving clinical outcomes and the overall experience for both patients and healthcare professionals.
The iToBoS project has successfully implemented a wide range of technological innovations that collectively improve the precision and efficiency of melanoma detection. Next, more relevant key achievements are presented.
Demonstration of the feasibility of using liquid lenses for non-contact dermatoscopic image acquisition
The use of liquid lenses in the total body scanner has been a pivotal innovation. These lenses enable the acquisition of high-resolution dermoscopic images without the need for physical contact. This technological advancement enables detailed visualization of skin lesions while ensuring a comfortable and non-invasive experience for patients.
Development of a total body scanner able to acquire non-contact dermoscopy
iToBoS project has developed an integrated total body scanning system designed to acquire high-resolution dermoscopic images without the need for direct contact with the patient’s skin. The system is equipped with advanced image processing and AI-driven analysis tools that enable automated detection and segmentation of skin lesions, streamlining the diagnostic workflow. This breakthrough in imaging technology marks a significant leap forward in early melanoma detection and has paved the way for more personalized and timely interventions.
Development of an AI cognitive assistant
A significant achievement has been the creation of a cognitive assistant powered by artificial intelligence. This tool can evaluate the specific malignancy risk of a skin lesion and providing a comprehensive assessment of the patient’s overall risk. The assistant integrates multiple data sources, including skin images, skin phenotype, demographic information, clinical data, and the presence of genetic mutations. This multifaceted analysis supports healthcare professionals by offering actionable insights tailored to each patient.
Development of an intelligent human-machine interface
To enhance the usability and clinical adoption of the AI cognitive assistant, the iToBoS project has developed an intelligent human-machine interface designed specifically for dermatologists. This interface presents the results of the cognitive assistant in a clear, structured and intuitive manner, ensuring that healthcare professionals can easily interpret and act upon the AI-generated insights. By employing advanced visualization techniques, the system effectively highlights key risk factors, lesion characteristics and patient-specific recommendations, facilitating a seamless integration into clinical workflows. This innovation bridges the gap between AI-driven analysis and medical decision-making, empowering dermatologists with a user-friendly tool that enhances diagnostic confidence and supports personalized patient care.
Integration of genomic data
The inclusion of genomic information, obtained through saliva samples, into the holistic risk score of a patient significantly enhances the predictive accuracy of melanoma risk assessments. By incorporating individual genetic markers, the project facilitates personalized risk stratification and enables early intervention strategies tailored to each patient’s unique profile. This genomic integration complements clinical and phenotypic data, resulting in a comprehensive assessment that considers both intrinsic and environmental factors.
Advanced tools for anonymization
To address concerns regarding data privacy and security, the project has incorporated robust anonymization tools. These tools not only safeguard patient confidentiality but also enhance the extraction of clinically relevant features from skin lesions, ensuring that the AI models operate with optimized inputs.
Secure cross-continental data exchange
A significant milestone of the iToBoS project is the successful establishment of secure data exchange protocols between Europe and Australia. Even though Australia is not governed by GDPR, data exchange tools have been developed while safeguarding patient data. This breakthrough not only preserves privacy and anonymity but also facilitates international collaboration by allowing researchers to integrate and analyse diverse datasets, ultimately enhancing melanoma detection and treatment strategies.
Contribution to advancing melanoma detection
The iToBoS project has made substantial contributions to improving the precision and personalization of melanoma diagnostics. By combining cutting-edge technologies and personalized approaches, the platform has transformed how melanoma risk is assessed and managed. Among the main contributions in the detection of melanoma, the following should be highlighted:
-
The integration of high-resolution imaging, genomic data, and phenotypic factors has enabled earlier and more accurate detection of suspicious lesions. This comprehensive approach allows for the evaluation of both the malignancy risk of individual lesions and the patient’s overall risk profile, ensuring a more holistic understanding of their condition.
-
An initial patient questionnaire was implemented as part of the project to collect detailed information about their skin phenotype, sun exposure history, and activities related to ultraviolet radiation. This data provided essential context for understanding environmental and behavioural factors that contribute to melanoma risk.