Publications by authors named "T V Bartolotta"
Article Synopsis
- Machine learning-based clinical decision support systems (CDSS) face challenges with transparency and reliability, as explainability often reduces predictive accuracy.
- A novel method called Rad4XCNN enhances the predictive power of CNN features while maintaining interpretability through Radiomics, moving beyond traditional saliency maps.
- In breast cancer classification tasks, Rad4XCNN demonstrates superior accuracy compared to other feature types and allows for global insights, effectively addressing the explainability-accuracy trade-off in AI models.
Performance of ultrasound-guided attenuation parameter and 2D shear wave elastography in patients with metabolic dysfunction-associated steatotic liver disease.
Eur Radiol
October 2024
Purpose: To assess the performance and the reproducibility of ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) in patients with biopsy-proven metabolic dysfunction-associated steatotic liver disease (MASLD).
Methods: This study included consecutive adult patients with MASLD who underwent ultrasound with UGAP, 2D-SWE and percutaneous liver biopsy. The median values of 12 consecutive UGAP measurements were acquired by two independent radiologists (R1 and R2).
Article Synopsis
- Rett syndrome (RTT) is a serious neurodevelopmental disorder primarily affecting females, leading to various neurologic impairments that significantly lower quality of life for both individuals and their caregivers.
- An international collaboration developed a caregiver questionnaire based on literature and parent interviews to assess 22 common issues related to RTT, using a 5-level Likert scale and anonymously surveying 756 caregivers.
- Results showed that communication and motor impairments were the most frequent and impactful issues, with analyses indicating that the severity of problems often exceeded the perceived impact on both patients and caregivers.
Purpose: To evaluate the role of multiparametric ultrasound (mpUS) in the characterization of focal breast lesions (FBLs).
Methods: This prospective study enrolled patients undergoing multiparametric breast ultrasound for FBLs. An experienced breast radiologist evaluated the following ultrasound features: US BI-RADS category, vascularization pattern (internal, vessels in rim and combined) and presence of penetrating vessels with each Doppler method (Color-Doppler, Power-Doppler, Microvascular imaging), strain ratio (SR) and Tsukuba score (TS) with Strain Elastography (SE), E, E, E and E with 2D-shear wave elastography (2D-SWE).
Purpose: To investigate the feasibility of an artificial intelligence (AI)-based semi-automated segmentation for the extraction of ultrasound (US)-derived radiomics features in the characterization of focal breast lesions (FBLs).
Material And Methods: Two expert radiologists classified according to US BI-RADS criteria 352 FBLs detected in 352 patients (237 at Center A and 115 at Center B). An AI-based semi-automated segmentation was used to build a machine learning (ML) model on the basis of B-mode US of 237 images (center A) and then validated on an external cohort of B-mode US images of 115 patients (Center B).