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Background: Pulmonary hypertension (PH) is a condition characterized by increased pressure in the pulmonary arteries with poor prognosis and, therefore, an optimal management is necessary. The study’s aim was to search for PH phenotypes and develop a predictive model of five-year mortality using machine learning (ML) algorithms. Methods: This multicenter study was conducted on 122 PH patients. Clinical and demographic data were collected and then used to identify phenotypes through clustering. Subsequently, a predictive model was performed by different ML algorithms. Results: Three PH clusters were identified: Cluster 1 (mean age 68.57 ± 10.54) includes 57% females, 69% from non-respiratory PH groups, and better cardiac (NYHA class 2.61 ± 0.84) and respiratory function (FEV1% 78.78 ± 21.54); Cluster 2 includes 50% females, mean age of 71.36 ± 8.32 years, 44% from PH group 3, worse respiratory function (FEV 1% 68.12 ± 10.20); intermediate cardiac function (NYHA class 3.18 ± 0.49) and significantly higher mortality (75%); Cluster 3 represents the youngest cluster (mean age 61.11 ± 13.50) with 65% males, 81% from non-respiratory PH groups, intermediate respiratory function (FEV1% 70.51 ± 17.91) and worse cardiac performance (NYHA class 3.22 ± 0.58). After testing ML models, logistic regression showed the best predictive performance (AUC = 0.835 and accuracy = 0.744) and identified three mortality-risk factors: age, NYHA class, and number of medications taken. Conclusions: The results suggest that the integration of ML into clinical practice can improve risk stratification to optimize treatment strategies and improve outcomes for PH patients.

INTRODUCTION: Pulmonary hypertension (PH) is a condition characterized by increased pressure in the pulmonary arteries with poor prognosis and, so, an optimal management is necessary. OBJECTIVES: The study aim was to search for PH phenotypes and develop a predictive model of five-year mortality using machine learning (ML) algorithms. METHODS: This multicenter study was conducted on 122 PH patients. Clinical and demographic data were collected and, then, used to identify phenotypes through clustering. Subsequently, predictive model was performed by different ML algorithms. RESULTS: Three PH clusters were identified: Cluster 1 (mean age 68,57±10,54) includes 57% females, 69% from non-respiratory PH groups, and better cardiac (NYHA class 2,61±0,84) and respiratory function (FEV1% 78,78±21,54); Cluster 2 includes 50% females, mean age of 71.36±8.32 years, 44% from PH group 3, worse respiratory function(FEV 1% 68,12±10,20);, intermediate cardiac function (NYHA class 3,18±0,49) and significantly higher mortality (75%); Cluster 3 represents the youngest cluster (mean age 61,11±13,50) with 65% males, 81% from non-respiratory PH groups, intermediate respiratory function (FEV1% 70,51±17,91) and worse cardiac performance (NYHA class 3,22±0,58);. After testing ML models, logistic regression showed the best predictive performance (AUC=0.835 and accuracy=0.744.) and identified three mortality-risk factors: age, NYHA class, number of medications taken. CONCLUSIONS: The results suggest that the integration of ML into clinical practice can improve risk stratification to optimize treatment strategies and improve the outcome of PH patients.

This study aimed to demonstrate the potential role of dual-energy CT in assessing bone mineral density (BMD) using hydroxyapatite–fat material pairing in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using a HAP–fat material pair. Statistical analysis was performed using the Bland–Altman method to assess the agreement between DXA and DECT HAP–fat measurements. Mean BMD, vertebral, and femoral T-scores were obtained. For vertebral analysis, the Bland–Altman plot showed an inverse correlation (R²: −0.042; RMSE: 0.690) between T-scores and DECT HAP–fat values for measurements from L1 to L4, while a good linear correlation (R²: 0.341; RMSE: 0.589) was found for measurements at the femoral neck. In conclusion, we demonstrate the enhanced importance of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

Colorectal cancer is very widespread in developed countries. Its diagnosis partly depends on pathologists’ experience and their laboratories’ instrumentation, producing uncertainty in diagnosis. The use of spectroscopic techniques sensitive to the cellular biochemical environment could aid in achieving a reliable diagnosis. So, we used Raman micro-spectroscopy, combined with a spectral analysis by means of machine learning methods, to build classification models, which allow colon cancer to be diagnosed in cell samples, in order to support such methods as complementary tools for achieving a reliable identification of colon cancer. The Raman spectra were analyzed in the 980–1800 cm⁻¹ range by focusing the laser beam onto the nuclei and the cytoplasm regions of single FHC and CaCo-2 cells (modelling healthy and cancerous samples, respectively) grown onto glass coverslips. The comparison of the Raman intensity of several spectral peaks and the Principal Component Analysis highlighted small biochemical differences between healthy and cancerous cells mainly due to the larger relative lipid content in the former cells with respect to the latter ones and to the larger relative amount of nucleic acid components in cancerous cells compared with healthy ones. We considered four classification algorithms (logistic regression, support vector machine, k nearest neighbors, and a neural network) to associate unknown Raman spectra with the cell type to which they belong. The built machine learning methods achieved median values of classification accuracy ranging from 95.5% to 97.1%, sensitivity values ranging from 95.5% to 100%, and specificity values ranging from 93.9% to 97.1%. The same median values of the classification parameters, which were estimated for a testing set including unknown spectra, ranged between 93.1% and 100% for accuracy and between 92.9% and 100% for sensitivity and specificity. A comparison of the four methods pointed out that k nearest neighbors and neural networks better perform the classification of nucleus and cytoplasm spectra, respectively. These findings are a further step towards the perspective of clinical translation of the Raman technique assisted by multivariate analysis as a support method to the standard cytological and immunohistochemical methods for diagnostic purposes.

Colorectal cancer was one of the most frequent causes of death due to cancer in 2020. Current diagnostic methods, based on colonoscopy and histological analysis of biopsy specimens, are partly dependent on the operator’s skills and expertise. In this study, we used Fourier transform infrared (FTIR) spectroscopy and different machine learning algorithms to evaluate the performance of such method as a complementary tool to reliably diagnose colon cancer. We obtained FTIR spectra of FHC and CaCo-2 cell lines originating from healthy and cancerous colon tissue, respectively. The analysis, based on the intensity values of specific spectral structures, suggested differences mainly in the content of lipid and protein components, but it was not reliable enough to be proposed as diagnostic tool. Therefore, we built six machine learning algorithms able to classify the two different cell types: CN2 rule induction, logistic regression, classification tree, support vector machine, k nearest neighbours, and neural network. Such models achieved classification accuracy values ranging from 87% to 100%, sensitivity from 88.1% to 100%, and specificity from 82.9% to 100%. By comparing the experimental data, the neural network resulted to be the model with the best performance parameters, having excellent values of accuracy, sensitivity, and specificity both in the low-wavenumber range (1000–1760 cm⁻¹) and in the high-wavenumber range (2700–3700 cm⁻¹). These results are encouraging for the application of the FTIR technique, assisted by machine learning algorithms, as a complementary diagnostic tool for cancer detection.

Cloud computing involves large numbers of computers connected through a network that can be physically located anywhere. Providers rely heavily on virtualization to deliver their cloud computing services. Cloud computing can reduce operational costs by using resources more efficiently. Starting with a look at cloud types, such as software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS), and cloud models such as public clouds, private clouds, hybrid clouds, community clouds, difference between a data center and cloud computing with relative advantages are explained, and finally why most of companies are migrating to this kind of technology. In the second part of chapter, the focus is on the risks and security of cloud computing. These complex architectures are highly vulnerable; therefore, it is proposed artificial intelligence (AI) models to protect them. Many system protection techniques have been realized to defend cloud-computing systems, but many works are in progress. The authors analyze the state of art on this subject. Request access from your librarian to read this chapter's full text.

Background: The accuracy of surgical guides is a relevant factor in both surgical safety and prosthetic implications. The impact of widespread fabrication technologies (milling and 3D printing) was investigated. Methods: Surgical guides manufactured by means of two specific milling and 3D-printing systems were digitized and compared in a 3D analysis with the digital file of the designed guides. The surface mean 3D distance (at the surface where the teeth and mucosa made contact) and the axial and linear deviations of the sleeves’ housings were measured by means of a metrological software program. Univariate and multivariate statistical analyses were used to investigate the effects of the fabrication technology, type of support, and arch type on the surgical guides’ accuracy. Results: The median deviations of the intaglio surface in contact with the mucosa were significantly (p < 0.001) lower for the milled surgical guides (0.05 mm) than for the 3D-printed guides (−0.07 mm), in comparison with the reference STL file. The generalized estimated equation models showed that the axial deviations of the sleeves’ housings (a median of 0.82 degrees for the milling, and 1.37 degrees for the 3D printing) were significantly affected by the fabrication technology (p = 0.011) (the milling exhibited better results), the type of support (p < 0.001), and the combined effect of the fabrication technology and the sleeve-to-crest angle (p = 0.003). The linear deviation (medians of 0.12 mm for the milling and 0.21 mm for the 3D printing) of their center points was significantly affected by the type of support (p = 0.001), with the milling performing slightly better than the 3D printing. Conclusions: The magnitude of the difference might account for a limited clinical significance.

As the global health care system grapples with steadily rising costs, increasing numbers of admissions, and the chronic defection of doctors and nurses from the profession, appropriate measures need to be put in place to reverse this course before it is too late