Foto del docente

Stefano Diciotti

Associate Professor

Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi"

Academic discipline: ING-INF/06 Electronic and Informatics Bioengineering

Research

Keywords: artificial intelligence machine learning brain complexity magnetic resonance imaging fractal analysis medical imaging computed tomography of the lungs

The main research themes are in the fields of computational medical imaging and artificial intelligence (AI) in Life Sciences:

- Fractal analysis for quantifying brain complexity during maturation, aging and in neurological disorders

Background: The brain is an anatomical structure that shows a high degree of statistical self- similarity and the fractal geometry describes the complexity of structures which show strict or statistical self-similarity in a proper interval of spatial scales. Fractal descriptors have the potential to represent a useful tool in detecting subtle brain changes as well as in tracking the progression of neurodegeneration.

Purpose: To design, develop, and apply novel fractal descriptors that reliable quantify the structural and functional complexity of the brain in healthy subjects and in patients with neurological disorders

- AI methods tailored for improving diagnostic accuracy and characterization of neurological disorders

Background: Clinical datasets have often a small size for deep learning applications. However, several AI techniques including transfer learning, generative adversarial networks, etc. enable to overcome, at least in part, this limitation.

Purpose: To design, develop, and apply dedicated AI methods for improving the training and generalization abilities of deep learning models in small size datasets in various neurological disorders.

- AI for augmented reality in robot-assisted surgery

Background: Three-dimensional models reconstructed from MRI and CT images are useful to guide the surgeon during crucial surgical steps. Recent research activities to improve 3D-guided robotic surgery focus on the development of augmented reality systems able to visualize 3D models in the surgical field with in vivo co-registration during robotic surgery. However, the 3D model does not follow automatically the real organ during its mobilization and traction during surgery. Thus, this non-automated augmented reality may increase the surgical time due to the need of several re-adjustments of the 3D model over the surgical field during the dissection.

Purpose: To design, develop, and validate an AI-based augmented reality system able to automatically co-register, in real time, a digital 3D-model of biological structures and the surgical field.

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