1,720,982 research outputs found
Artificial intelligence techniques support nuclear medicine modalities to improve the diagnosis of Parkinson's disease and Parkinsonian syndromes
No full textPurpose The aim of this review is to discuss the most significant contributions about the role of Artificial Intelligence (AI) techniques to support the diagnosis of movement disorders through nuclear medicine modalities. Methods The work is based on a selection of papers available on PubMed, Scopus and Web of Sciences. Articles not written in English were not considered in this study. Results Many papers are available concerning the increasing contribution of machine learning techniques to classify Parkinson's disease (PD), Parkinsonian syndromes and Essential Tremor (ET) using data derived from brain SPECT with dopamine transporter radiopharmaceuticals. Other papers investigate by AI techniques data obtained by 123I-MIBG myocardial scintigraphy to differentially diagnose PD and other Parkinsonian syndromes. Conclusion The recent literature provides strong evidence that AI techniques can play a fundamental role in the diagnosis of movement disorders by means of nuclear medicine modalities, therefore paving the way towards personalized medicine
Addressing pose estimation issues for machine vision based UAV autonomous serial refuelling
No full textThis paper describes the results of an effort on the analysis of the performance of specific 'pose estimation' algorithms within a Machine Vision-based approach for the problem of aerial refuelling for unmanned aerial vehicles. The approach assumes the availability of a camera on the unmanned aircraft for acquiring images of the refuelling tanker; also, it assumes that a number of active or passive light sources - the 'markers' - are installed at specific known locations on the tanker. A sequence of machine vision algorithms on the on-board cornputer of the unmanned aircraft is tasked with the processing of the images of the tanker. Specifically, detection and labeling algorithms are used to detect and identify the markers and a 'pose estimation' algorithm is used to estimate the relative position and orientation between the two aircraft.
Detailed closed-loop simulation studies have been performed to compare the performance of two 'pose estimation' algorithms within a simulation environment that was specifically developed for the study of aerial refuelling problems. Special emphasis is placed on the analysis of the required computational effort as well as on the accuracy and the error propagation characteristics of the two methods. The general trade offs involved in the selection of the pose estimation algorithm are discussed. Finally, simulation results are presented and analysed
123-I-MIBG cardiac scintigraphy quantitative analysis in Parkinson's disease (PD) and Parkinsonism (P) differential diagnosis: a classification tree (CIT) classifier additional contribute
No full textComparison of two neural networks classifiers in the differential diagnosis of essential tremor and Parkinson’s disease by (123)I-FP-CIT brain SPECT
No full textPURPOSE: To contribute to the differentiation of Parkinson's disease (PD) and
essential tremor (ET), we compared two different artificial neural network
classifiers using (123)I-FP-CIT SPECT data, a probabilistic neural network (PNN)
and a classification tree (ClT).
METHODS: (123)I-FP-CIT brain SPECT with semiquantitative analysis was performed
in 216 patients: 89 with ET, 64 with PD with a Hoehn and Yahr (H&Y) score of ≤2
(early PD), and 63 with PD with a H&Y score of ≥2.5 (advanced PD). For each of
the 1,000 experiments carried out, 108 patients were randomly selected as the PNN
training set, while the remaining 108 validated the trained PNN, and the
percentage of the validation data correctly classified in the three groups of
patients was computed. The expected performance of an "average performance PNN"
was evaluated. In analogy, for ClT 1,000 classification trees with similar
structures were generated.
RESULTS: For PNN, the probability of correct classification in patients with
early PD was 81.9±8.1% (mean±SD), in patients with advanced PD 78.9±8.1%, and in
ET patients 96.6±2.6%. For ClT, the first decision rule gave a mean value for the
putamen of 5.99, which resulted in a probability of correct classification of
93.5±3.4%. This means that patients with putamen values >5.99 were classified as
having ET, while patients with putamen values <5.99 were classified as having PD.
Furthermore, if the caudate nucleus value was higher than 6.97 patients were
classified as having early PD (probability 69.8±5.3%), and if the value was <6.97
patients were classified as having advanced PD (probability 88.1%±8.8%).
CONCLUSION: These results confirm that PNN achieved valid classification results.
Furthermore, ClT provided reliable cut-off values able to differentiate ET and PD
of different severities
Evaluation of the performance of two methods of semi-quantitative analysis of 123I-FP-CIT brain SPECT data by means of a support vector machine classifier (SVM) to diagnose Parkinson’s disease.
No full textDesign and flight-testing of non-linear formation control laws
No full textThis paper presents the results of a research effort focused on the modeling, identification, control design, simulation, and flight-testing of YF-22 research aircraft models in closed-loop formation. These models were designed, manufactured, and instrumented at West Virginia University (WVU). The first phase of flight tests was performed with the goal of exciting all the aircraft dynamic modes. The recorded flight data were then used for a parameter identification study. The output of this Study was a mathematical model of the WVU YF-22 aircraft, which was then used for the design of the formation control laws. The design of the formation control laws is based on an inner/outer loop design with the objective of controlling the forward, lateral, and vertical distances between two aircraft in the formation. The design for the outer loop scheme was based on feedback linearization while a root locus-based approach was used for the design of the inner loop scheme. The paper presents experimental results validating the performance of the formation control laws using a 'virtual leader' configuration
SPECT and PET serve as molecular imaging techniques and in vivo biomarkers for brain metastases
No full textNuclear medicine techniques (single photon emission computerized tomography, SPECT, and positron emission tomography, PET) represent molecular imaging tools, able to provide in vivo biomarkers of different diseases. To investigate brain tumours and metastases many different radiopharmaceuticals imaged by SPECT and PET can be used. In this review the main and most promising radiopharmaceuticals available to detect brain metastases are reported. Furthermore the diagnostic contribution of the combination of SPECT and PET data with radiological findings (magnetic resonance imaging, MRI) is discussed
123I-MIBG cardiac scintigraphy in discriminating Parkinson’s disease (PD) from vascular Parkinsonism (VP ) using classification tree( CIT ) classifier together with conventional quantitative analysis.
No full text123I-MIBG cardiac scintigraphy quantitative analysis in Parkinson’s disease (PD) and Parkinsonisms (P) differential diagnosis: a classification tree (CIT) classifier additional contribute.
No full textCut-off values to diagnose Parkinson’s disease by means of 123I-FP-CIT brain SPECT semiquantitative data as evaluated by classificatory tree algorithm.
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