Publications by authors named "S Tangaro"
Antimicrobial resistance refers to the ability of pathogens to develop resistance to drugs designed to eliminate them, making the infections they cause more difficult to treat and increasing the likelihood of disease diffusion and mortality. As such, antimicrobial resistance is considered as one of the most significant and universal challenges to both health and society, as well as the environment. In our research, we employ the explainable artificial intelligence paradigm to identify the factors that most affect the onset of antimicrobial resistance in diversified territorial contexts, which can vary widely from each other in terms of climatic, economic and social conditions.
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- Medical imaging research using AI is hindered by the lack of large datasets, resulting in challenges like overfitting due to small sample sizes.
- A systematic review of 147 peer-reviewed articles revealed that many studies applied transfer learning and data augmentation techniques, while adherence to reporting standards was notably low.
- The review aims to highlight recent strategies to address small sample sizes, advocate for better transparency and quality in medical imaging publications, and encourage compliance with established reporting guidelines.
Purpose: A novel and unconventional approach to a machine learning challenge was designed to spread knowledge, identify robust methods and highlight potential pitfalls about machine learning within the Medical Physics community.
Methods: A public dataset comprising 41 radiomic features and 535 patients was employed to assess the potential of radiomics in distinguishing between primary lung tumors and metastases. Each participant developed two classification models using: (i) all features (base model); (ii) only robust features (robust model).
Article Synopsis
- - Advances in DNA sequencing have transformed plant genomics, but predicting plant traits (phenotypes) from genetic data is still difficult, especially in breeding contexts; this study aims to improve prediction accuracy by using explainable AI with machine learning.
- - The research compared various machine learning methods to predict the almond shelling fraction using data from an almond collection, revealing that the Random Forest method provided the best predictions and identified important genetic regions linked to the trait.
- - The study demonstrated that explainable AI not only improves the understanding of genetic factors related to phenotypes but also plays a crucial role in enhancing crop production in sustainable agriculture.
Autism spectrum disorder (ASD) affects social interaction and communication. Emerging evidence links ASD to gut microbiome alterations, suggesting that microbial composition may play a role in the disorder. This study employs explainable artificial intelligence (XAI) to examine the contributions of individual microbial species to ASD.
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