Circuital modelling in muscle tissue impedance measurements.

Electrical impedance spectroscopy (EIS) stands as a widely employed characterization technique for studying muscular tissue in both physio/pathological conditions. This methodology commonly involves modeling tissues through equivalent electrical circuits, facilitating a correlation between electrical parameters and physiological properties. Within existing literature, diverse equivalent electrical circuits have been proposed, varying in complexity and fitting properties. However, to date, none have definitively proven to be the most suiTable for tissue impedance measurements. This study aims to outline a systematic methodology for EIS measurements and to compare the performances of three widely used electrical circuits in characterizing both physiological and pathological muscle tissue conditions. Results highlight that, for optimal fitting with electrical parameters relevant to tissue characterization, the choice of the circuit to be fitted closely hinges on the specific measurement objectives, including measurement parameters and associated physiological features. Naturally, this necessitates a balance between simplicity and fitting accuracy.

A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions.

Despite mammography (MG) being among the most widespread techniques in breast cancer screening, tumour detection and classification remain challenging tasks due to the high morphological variability of the lesions. The extraction of radiomics features has proved to be a promising approach in MG. However, radiomics features can suffer from dependency on factors such as acquisition protocol, segmentation accuracy, feature extraction and engineering methods, which prevent the implementation of robust and clinically reliable radiomics workflow in MG. In this study, the variability and robustness of radiomics features is investigated as a function of lesion segmentation in MG images from a public database. A statistical analysis is carried out to assess feature variability and a radiomics robustness score is introduced based on the significance of the statistical tests performed. The obtained results indicate that variability is observable not only as a function of the abnormality type (calcification and masses), but also among feature categories (first-order and second-order), image view (craniocaudal and medial lateral oblique), and the type of lesions (benign and malignant). Furthermore, through the proposed approach, it is possible to identify those radiomics characteristics with a higher discriminative power between benign and malignant lesions and a lower dependency on segmentation, thus suggesting the most appropriate choice of robust features to be used as inputs to automated classification algorithms.

Diagnostic Applications of Intraoral Scanners: A Systematic Review.

In addition to their recognized value for obtaining 3D digital dental models, intraoral scanners (IOSs) have recently been proven to be promising tools for oral health diagnostics. In this work, the most recent literature on IOSs was reviewed with a focus on their applications as detection systems of oral cavity pathologies. Those applications of IOSs falling in the general area of detection systems for oral health diagnostics (e.g., caries, dental wear, periodontal diseases, oral cancer) were included, while excluding those works mainly focused on 3D dental model reconstruction for implantology, orthodontics, or prosthodontics. Three major scientific databases, namely Scopus, PubMed, and Web of Science, were searched and explored by three independent reviewers. The synthesis and analysis of the studies was carried out by considering the type and technical features of the IOS, the study objectives, and the specific diagnostic applications. From the synthesis of the twenty-five included studies, the main diagnostic fields where IOS technology applies were highlighted, ranging from the detection of tooth wear and caries to the diagnosis of plaques, periodontal defects, and other complications. This shows how additional diagnostic information can be obtained by combining the IOS technology with other radiographic techniques. Despite some promising results, the clinical evidence regarding the use of IOSs as oral health probes is still limited, and further efforts are needed to validate the diagnostic potential of IOSs over conventional tools.

Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography.

BACKGROUND: breast cancer (BC) is the world's most prevalent cancer in the female population, with 2.3 million new cases diagnosed worldwide in 2020. The great efforts made to set screening campaigns, early detection programs, and increasingly targeted treatments led to significant improvement in patients' survival. The Full-Field Digital Mammograph (FFDM) is considered the gold standard method for the early diagnosis of BC. From several previous studies, it has emerged that breast density (BD) is a risk factor in the development of BC, affecting the periodicity of screening plans present today at an international level. OBJECTIVE: in this study, the focus is the development of mammographic image processing techniques that allow the extraction of indicators derived from textural patterns of the mammary parenchyma indicative of BD risk factors. METHODS: a total of 168 patients were enrolled in the internal training and test set while a total of 51 patients were enrolled to compose the external validation cohort. Different Machine Learning (ML) techniques have been employed to classify breasts based on the values of the tissue density. Textural features were extracted only from breast parenchyma with which to train classifiers, thanks to the aid of ML algorithms. RESULTS: the accuracy of different tested classifiers varied between 74.15% and 93.55%. The best results were reached by a Support Vector Machine (accuracy of 93.55% and a percentage of true positives and negatives equal to TPP = 94.44% and TNP = 92.31%). The best accuracy was not influenced by the choice of the features selection approach. Considering the external validation cohort, the SVM, as the best classifier with the 7 features selected by a wrapper method, showed an accuracy of 0.95, a sensitivity of 0.96, and a specificity of 0.90. CONCLUSIONS: our preliminary results showed that the Radiomics analysis and ML approach allow us to objectively identify BD.

Patient safety implications of wearing a face mask for prevention in the era of COVID-19 pandemic: a systematic review and consensus recommendations.

In the past, the use of face masks in western countries was essentially limited to occupational health. Now, because of the COVID-19 pandemic, mask-wearing has been recommended as a public health intervention. As potential side effects and some contraindications are emerging, we reviewed the literature to assess the impact of them in daily life on patient safety and to provide appropriate guidelines and recommendations. We performed a systematic review of studies investigating physiological impact, safety, and risk of masks in predefined categories of patients, which have been published in peer-reviewed journals with no time and language restrictions. Given the heterogeneity of studies, results were analyzed thematically. We used PRISMA guidelines to report our findings. Wearing a N95 respirator is more associated with worse side effects than wearing a surgical mask with the following complications: breathing difficulties (reduced FiO2, SpO2, PaO2 increased ETCO2, PaCO2), psychiatric symptoms (panic attacks, anxiety) and skin reactions. These complications are related to the duration of use and/or disease severity. Difficulties in communication is another issue to be considered especially with young children, older person and people with hearing impairments. Even if benefits of wearing face masks exceed the discomfort, it is recommended to take an "air break" after 1-2 h consecutively of mask-wearing. However, well-designed prospective studies are needed. The COVID-19 pandemic could represent a unique opportunity for collecting large amount of real-world data.