Body condition assessment using digital images.

This project assessed the ability to assign a body condition score (BCS) to a dairy cow from digital photographs or videos. Images were taken from the rear of the cow at a 0 to 20 degrees angle relative to the tail head. Four observers assigned a BCS to each of 57 cows at a farm visit (live, farm 1) and later from a photograph (photo). Means +/- standard deviations of BCS by method and observer were as follows: live = 3.25 +/- 0.51, 3.42 +/- 0.49, 3.32 +/- 0.58, 3.13 +/- 0.62; photo = 3.36 +/- 0.52, 3.32 +/- 0.43, 3.44 +/- 0.62, 3.14 +/- 0.6 for observers 1 to 4, respectively. Body condition score means differed across observers for live (observer 2 higher and observer 4 lower, compared with observers 1 and 3) and photo methods (observer 3 lower, compared with observers 1, 2, and 3); however, within observer, the mean live BCS did not differ from the mean photo BCS. Correlation coefficients between BCS assigned live and from photos were 0.84, 0.82, 0.82, and 0.90 for observers 1 to 4, respectively. Subsequently, observer 1 visited 2 farms, assigned a live BCS, and digitally photographed 187 cows (56 and 131 cows from farms 2 and 3, respectively). Observers 2, 3, and 4 assigned a BCS from the photographs. Means +/- standard deviations of BCS by observer (method) were 1 (live) 3.35 +/- 0.55; 2 (photo) 3.33 +/- 0.49; 3 (photo) 3.60 +/- 0.54; and 4 (photo) 3.26 +/- 0.62. The mean BCS for observer 3 was higher and that for observer 4 was lower than for observers 1 and 2. Correlation coefficients between observer 1 and observers 2 through 4 were 0.78, 0.76, and 0.79, respectively. Observer 1 assigned a BCS to 41 cows at a farm visit and 3 wk later assessed the BCS of cows from a video taken at a farm visit by a different individual. Cows were restrained in headlocks at a feed bunk when assessing BCS and for video production. No difference was detected for the mean BCS, for the standard deviation of the mean BCS, or in the distribution of BCS between the live and video assessments. Mean and SD for 17 groups of Holstein cows from 20 farms were used to generate 10,000 random samples of BCS. Groups of 25, 50, 100, and 150 cows were created from the random samples, and estimates of mean BCS were determined by sampling 3 to 80% of the group. Estimates of mean BCS with a sample size of 30% or more from a group of cows fell within the 95% confidence limit of the true mean more than 98% of the time. Digital photographs provide adequate imaging for assessment of BCS. Sampling 30% of a group should be adequate to assess the mean BCS. Video imaging allowed a rapid assessment of BCS but did not permit identification of individual cows

Objective estimation of body condition score by modeling cow body shape from digital images.

Body condition score (BCS) is considered an important tool for management of dairy cattle. The feasibility of estimating the BCS from digital images has been demonstrated in recent work. Regression machines have been successfully employed for automatic BCS estimation, taking into account information of the overall shape or information extracted on anatomical points of the shape. Despite the progress in this research area, such studies have not addressed the problem of modeling the shape of cows to build a robust descriptor for automatic BCS estimation. Moreover, a benchmark data set of images meant as a point of reference for quantitative evaluation and comparison of different automatic estimation methods for BCS is lacking. The main objective of this study was to develop a technique that was able to describe the body shape of cows in a reconstructive way. Images, used to build a benchmark data set for developing an automatic system for BCS, were taken using a camera placed above an exit gate from the milking robot. The camera was positioned at 3 m from the ground and in such a position to capture images of the rear, dorsal pelvic, and loin area of cows. The BCS of each cow was estimated on site by 2 technicians and associated to the cow images. The benchmark data set contained 286 images with associated BCS, anatomical points, and shapes. It was used for quantitative evaluation. A set of example cow body shapes was created. Linear and polynomial kernel principal component analysis was used to reconstruct shapes of cows using a linear combination of basic shapes constructed from the example database. In this manner, a cow's body shape was described by considering her variability from the average shape. The method produced a compact description of the shape to be used for automatic estimation of BCS. Model validation showed that the polynomial model proposed in this study performs better (error=0.31) than other state-of-the-art methods in estimating BCS even at the extreme values of BCS scale

An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease

The increment of the prevalence of neurological diseases due to the trend in population aging demands for new strategies in disease management. In Parkinson's disease (PD), these strategies should aim at improving diagnosis accuracy and frequency of the clinical follow-up by means of decentralized cost-effective solutions. In this context, a system suitable for the remote monitoring of PD subjects is presented. It consists of the integration of two approaches investigated in our previous works, each one appropriate for the movement analysis of specific parts of the body: low-cost optical devices for the upper limbs and wearable sensors for the lower ones. The system performs the automated assessments of six motor tasks of the unified Parkinson's disease rating scale, and it is equipped with a gesture-based human machine interface designed to facilitate the user interaction and the system management. The usability of the system has been evaluated by means of standard questionnaires, and the accuracy of the automated assessment has been verified experimentally. The results demonstrate that the proposed solution represents a substantial improvement in PD assessment respect to the former two approaches treated separately, and a new example of an accurate, feasible and cost-effective mean for the decentralized management of PD

A Self-Managed System for Automated Assessment of UPDRS Upper Limb Tasks in Parkinson’s Disease

A home-based, reliable, objective and automated assessment of motor performance of patients affected by Parkinson’s Disease (PD) is important in disease management, both to monitor therapy efficacy and to reduce costs and discomforts. In this context, we have developed a self-managed system for the automated assessment of the PD upper limb motor tasks as specified by the Unified Parkinson’s Disease Rating Scale (UPDRS). The system is built around a Human Computer Interface (HCI) based on an optical RGB-Depth device and a replicable software. The HCI accuracy and reliability of the hand tracking compares favorably against consumer hand tracking devices as verified by an optoelectronic system as reference. The interface allows gestural interactions with visual feedback, providing a system management suitable for motor impaired users. The system software characterizes hand movements by kinematic parameters of their trajectories. The correlation between selected parameters and clinical UPDRS scores of patient performance is used to assess new task instances by a machine learning approach based on supervised classifiers. The classifiers have been trained by an experimental campaign on cohorts of PD patients. Experimental results show that automated assessments of the system replicate clinical ones, demonstrating its effectiveness in home monitoring of PD

Distribution pattern of hepatitis C virus genotypes and correlation with viral load and risk factors in chronic positive patients.

Objective: Hepatitis C virus (HCV) has emerged as a leading cause of chronic hepatitis, liver cirrhosis and hepatocellular carcinoma worldwide. The purpose of this study was to describe the distribution pattern of HCV genotypes in chronic hepatitis patients in the Campania region of southern Italy and estimate their association with risk factors and viral load. Materials and Methods: 404 consecutive HCV ribonucleic acid-positive patients were included in the study. HCV genotyping was carried out by the HCV line probe assay test and viral load estimation by the TaqMan real-time PCR system. Results: The predominant genotype was 1 (63.6%), followed by genotype 2 (29.4%), 3 (6.2%) and 4 (0.8%). Subtype 1b was more frequent in females than in males. Conversely, genotype 3 was more frequent in males. No significant difference was observed in age distribution of HCV genotypes. Surgery and dental therapy were the most frequent risk factors for genotype 1 and intravenous drug abuse and tattooing for genotype 3. Patients with genotype 1 more frequently showed high HCV viral load when compared to those with genotypes 2 and 3. Conclusion: The present study revealed that HCV genotypes 1 and 2 accounted for over 95% of all HCV infections in the Campania region, and genotype 1 was more frequently associated with a higher viral load when compared to genotypes 2 and 3

Management of heart failure in Piedmont Region

emerging problem in industrialized countries: it continues to be diagnosed at high rates and has an decreased survival time, raising new problems, such as the need of an adequate medical service organization and resource expenditure. Aim of this analysis was a quantitative evaluation of diagnostic and therapeutic resource use for CHF in outpatient departments in Piedmont, Italy. Methods. We performed a cross-sectional observational study, based on a two-month data collection in 12 outpatient departments dedicated to congestive heart failure. Information was obtained on each patient using a specific anonymous data collection form. Results. We obtained and analyzed for the study 547 forms. Mean patient age was 66.1 years, mean ejection fraction was 36.6%. Coronary artery disease accounted for 34.6% of congestive heart failure cases, followed by idiopathic etiology (26.4%). Main comorbidities were diabetes (22.3%) and chronic obstructive pulmonary disease (17.7%). Sixty-nine% of patients received a medical treatment with angiotensin-converting enzyme (ACE) inhibitors, 72.6% with β-blockers, 48.8% with aldosterone antagonists. As far as diagnostic resource use during a six-month period preceeding observation, 46.8% of patients underwent echocardiographic examination, 9.9% Holter ECG, 6.0% coronary angiography. Therapy was more often increased in patients who underwent an instrumental evaluation during the preceeding six-month period. Conclusions. Data suggests that in Piedmont outpatients with chronic heart failure receive a high drug prescription level and a small number of instrumental evaluations, as suggested in main international guidelines