Search results for "machine learning"
showing 10 items of 1464 documents
Appendectomy during the COVID-19 pandemic in Italy: a multicenter ambispective cohort study by the Italian Society of Endoscopic Surgery and new tech…
2021
AbstractMajor surgical societies advised using non-operative management of appendicitis and suggested against laparoscopy during the COVID-19 pandemic. The hypothesis is that a significant reduction in the number of emergent appendectomies was observed during the pandemic, restricted to complex cases. The study aimed to analyse emergent surgical appendectomies during pandemic on a national basis and compare it to the same period of the previous year. This is a multicentre, retrospective, observational study investigating the outcomes of patients undergoing emergent appendectomy in March–April 2019 vs March–April 2020. The primary outcome was the number of appendectomies performed, classifie…
Neural Networks Ensemble for Cyclosporine Concentration Monitoring
2001
This paper proposes the use of neural networks ensemble for predicting the cyclosporine A (CyA)concen tration in kidney transplant patients. In order to optimize clinical outcomes and to reduce the cost associated with patient care, accurate prediction of CyA concentrations is the main objective of therapeutic drug monitoring. Thirty-two renal allograft patients and different factors (age, weight, gender, creatinine and post-transplantation days, together with past dosages and concentrations)w ere studied to obtain the best models. Three kinds of networks (multilayer perceptron, FIR network, Elman recurrent network) and the formation of neural-network ensembles were used. The FIR network, y…
Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges
2021
Abstract The application of imaging biomarkers in oncology is still in its infancy, but with the expansion of radiomics and radiogenomics a revolution is expected in this field. This may be of special interest in head and neck cancer, since it can promote precision medicine and personalization of treatment by overcoming several intrinsic obstacles in this pathology. Our goal is to provide the medical oncologist with the basis to approach these disciplines and appreciate their main uses in clinical research and clinical practice in the medium term. Aligned with this objective we analyzed the most relevant studies in the field, also highlighting novel opportunities and current challenges.
Editorial for the Special Issue “Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions”
2019
This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multimodal data was used in object analysis.
Comparison between statistical and fuzzy approaches for improving diagnostic decision making in patients with chronic nasal symptoms
2014
This paper compares a fuzzy model, expressed in rule-form, with a well known statistical approach (i.e. logistic regression model) for diagnostic decision making in patients with chronic nasal symptoms. The analyses were carried out using a database obtained from a questionnaire administered to 1359 patients with nasal symptoms containing personal data, clinical data and skin prick test (SPT) results. Both the fuzzy model and the logistic regression model developed were validated using a data set obtained from another medical institution. The accuracy of the two models in identifying patients with positive or negative SPT was similar. This study is a preliminary step to the creation of a so…
Advanced technologies for detecting tremor in Parkinson's disease.
2019
Objective Accurate and reliable detection of tremor onset in Parkinson’s disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning methods for more accurate detection of rest tremor in PD. Methods We analyzed the local field potential (LFP) recordings from the subthalamic nucleus region in 12 patients with PD (16 recordings). To explore the optimal biomarkers and the best performing classifier, the performance of state-of-the-art machine learning (ML) algorithms and various features of the subthalamic LFPs were compared. We further used a Kalman filtering technique in feature…
The role of PET radiomic features in prostate cancer: a systematic review
2021
Aim: This systematic review aims to present the available evidence on the use of radiomic features (RFs) extracted from PET imaging in patients with prostate cancer (PCa). Materials and methods: A comprehensive literature search of studies on the utility of PET-derived RFs in patients with PCa was performed in the PubMed/MEDLINE database through February 24th, 2021 using the following search string: [“positron-emission tomography” (MeSh terms) OR “positron emission tomography computed tomography” (MeSh terms) OR “positron-emission tomography” (all fields) OR “positron emission tomography computed tomography” (all fields) OR “PET” (all fields)] AND [“radiomics” (all fields) OR “radiomic” (al…
Assessment of microalgae species, biomass, and distribution from spectral images using a convolution neural network
2022
AbstractEffective monitoring of microalgae growth is crucial for environmental observation, while the applications of this monitoring could also be expanded to commercial and research-focused microalgae cultivation. Currently, the distinctive optical properties of different microalgae groups are targeted for monitoring. Since different microalgae can grow together, their spectral signals are mixed with ambient properties, making estimations of species biomasses a challenging task. In this study, we cultured five different microalgae and monitored their growth with a mobile spectral imager in three separate experiments. We trained and validated a one-dimensional convolution neural network by…
Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars
2021
Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including p…
An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning
2015
Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth’s surface and their interactions with vegetation and atmosphere. When it comes to studying vegetation properties, RTMs allows us to study light interception by plant canopies and are used in the retrieval of biophysical variables through model inversion. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. Emulators are advantageous in real practice because…