More on perspectives for Generative AI-Assisted Art Therapy for melanoma patients

The COVID-19 pandemic accelerated the adoption of digital technologies in many areas of medicine.

Dataset Description for the iToBoS 2024 - Skin Lesion Detection with 3D-TBP

In this blog we present more details about the iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection.

Genetic Testing for Familial Melanoma

iToBoS research partners from The University of Queensland have recently published an invited article with the Italian Journal of Dermatology and Venereology, titled ‘Genetic testing for familial melanoma’. 

The hospital operator interface of the first scanner prototype

The Hospital Operator Interface, or HMI, plays a crucial role in the operation of the scanner.

Development of a cloud-based AI cognitive assistant for holistic melanoma risk estimation

An AI cognitive assistant is developed that will fuse information from multiple data sources, providing melanoma risk estimation both on the patient level as well as per-lesion.

High-Resolution Imaging Module for precise skin condition monitoring

The initial and crucial step in total body imaging involves the optical imaging unit. Illumination is provided by 10 high-brightness LEDs, delivering over 150,000 lumens for uniformly distributed lighting.

Patient protection in the first scanner prototype

In developing the "Arch" prototype of the scanner, patient safety was paramount.

Development and publication of the clinical study protocol for transparency and reproducibility

To date, majority of AI tools for skin cancer monitoring only assess single lesion images, in isolation of any patient clinical background.  

Why is important the use of 3D cameras?

3D cameras gather detailed information about the three-dimensional shape of the patient being scanned.

Development of novel algorithms for quantitative risk assessment based on clinical and imaging phenotyping data

Quantitative risk estimation based on clinical data, typically collected through patient questionnaires, is based on linear regression models that are readily interpretable by clinicians.