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The Italian Government planned to invest €15 billion of European funds on National Health Service digitalization and primary care enhancement. The critical burden brought by the pandemic upon hospital care mean these investments could no longer be delayed, considering the extraordinary backlogs of many treatments and the ordinary gaps of fragmented long-term care, in Italy and abroad. National guidelines have been published to standardize interventions across the Italian regions, and telemedicine is frequently mentioned as a key innovation to achieve both goals. The professional resources needed to run the facilities introduced in primary care are defined with great precision, but no details are given on how digitalization and remote care technologies must be implemented in this context. Building on this policy case, this paper focuses on what contribution digitalization and telemedicine can offer to specific primary care innovations, drawing from implemented technology-driven policies which may support the effective stratification, prevention and management of chronic patient needs, including anticipatory healthcare, population health management, adjusted clinical groups, chronic care management, quality and outcomes frameworks, patient-reported outcomes and patient-reported experience. All these policies can benefit significantly from digitalization and remote care technology, provided that some risks and limitations are considered by design. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterinary clinics in Italy, which were evaluated for image quality by three experienced veterinary diagnostic imagers. The algorithm was designed to classify the images as correct or having one or more of the following errors: rotation, underexposure, overexposure, incorrect limb positioning, incorrect neck positioning, blurriness, cut-off, or the presence of foreign objects, or medical devices. The algorithm was able to correctly identify errors in thoracic radiographs with an overall accuracy of 81.5% in latero-lateral and 75.7% in sagittal images. The most accurately identified errors were limb mispositioning and underexposure both in latero-lateral and sagittal images. The accuracy of the developed model in the classification of technically correct radiographs was fair in latero-lateral and good in sagittal images. The authors conclude that their AI-based algorithm is a promising tool for improving the accuracy of radiographic interpretation by identifying technical errors in canine thoracic radiographs. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterinary clinics in Italy, which were evaluated for image quality by three experienced veterinary diagnostic imagers. The algorithm was designed to classify the images as correct or having one or more of the following errors: rotation, underexposure, overexposure, incorrect limb positioning, incorrect neck positioning, blurriness, cut-off, or the presence of foreign objects, or medical devices. The algorithm was able to correctly identify errors in thoracic radiographs with an overall accuracy of 81.5% in latero-lateral and 75.7% in sagittal images. The most accurately identified errors were limb mispositioning and underexposure both in latero-lateral and sagittal images. The accuracy of the developed model in the classification of technically correct radiographs was fair in latero-lateral and good in sagittal images. The authors conclude that their AI-based algorithm is a promising tool for improving the accuracy of radiographic interpretation by identifying technical errors in canine thoracic radiographs. [ABSTRACT FROM AUTHOR]

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A reliable prediction of the spatio-temporal drought variation can lead to a reduction in vulnerability and an improvement in the management of drought-dependent businesses. In this study, three clustering algorithms, K-mean, Hierarchical and Expectation–Maximization, were first used to divide Southern Italy into homogeneous drought regions, based on gridded data of the Standardized Precipitation Evapotranspiration Index forecasting with a 6 months' time scale (SPEI6). The Hierarchical algorithm identified five well-distinct clusters characterized by drought events of different duration and severity, considering the different morphoclimatic characteristics of the study area. Then, the mean SPEI6 time series were evaluated for each cluster and used to assess the evolutionary drought trends. In addition, two Machine Learning (ML) algorithms, M5P and Support Vector Regression (SVR), were also used to develop forecasting models for the SPEI6, without the need for additional exogenous inputs. Moreover, the Stacking ML technique was used to develop a hybrid model based on both individual M5P and SVR algorithms. The clustering-forecasting combination makes it possible to identify the evolutionary trends taking place in the various homogeneous areas in a concise but effective manner. The hybrid M5P-SVR model (R2 up to 0.91, minimum Root Mean Square Error RMSE = 0.38) outperformed both M5P (R2 up to 0.87, minimum RMSE = 0.42) and SVR (R2 up to 0.89, minimum RMSE = 0.39) models, showing to be particularly suitable for drought forecasting in areas with long and severe drought events. [ABSTRACT FROM AUTHOR]

Background: To investigate the potential use of Artificial Neural Network (ANN) in the evaluation of serum protein electrophoresis, we set up a multicenter study involving six Italian laboratories. For this purpose, we developed an algorithm named CASPER (Computer Assisted Serum Protein Electrophoresis Recognizer). Methods: A total of 59,516 samples from the six centers were divided into three groups. Training and validation sets were used to develop the neural network, whereas evaluation set was used to test the performance of CASPER in recognizing abnormal electrophoretic profiles. Results: CASPER showed 93.0% sensitivity and 47.4% specificity. CASPER sensitivity and specificity ranged in the six sites from 88% (site 3) to 97% (site 5) and from 36% (site 6) to 53% (site 3), respectively. Sensitivity for γ zone was 94.6%, for β zone 89.7% and for oligoclonal patterns 92.0%. Conclusions: The sensitivity of the CASPER algorithm does not allow us to recommend its use as a replacement for the visual inspection, but it could be helpful in avoiding accidental misclassifications by the operator. Moreover, the CASPER algorithm may be a useful tool for training operators and students. This study evidenced a high inter-observer variability, which should be addressed in a dedicated study. Data set to train and validate ANNs should contain a huge range and an adequate number of different abnormalities. Clin Chem Lab Med 2008;46:1183–8. [ABSTRACT FROM AUTHOR]