• Health technology assessment through Six Sigma Methodology to assess cemented and uncemented protheses in total hip arthroplasty.

      Latessa, Imma; Ricciardi, Carlo; Jacob, Deborah; Jónsson, Halldór; Gambacorta, Monica; Improta, Giovanni; Gargiulo, Paolo; 1University Hospital of Naples "Federico II", Department of Public Health, Naples, Italy; Reykjavík University, Institute for Biomedical and Neural Engineering, Reykjavík. immalatessa@gmail.com. 2Reykjavík University, Institute for Biomedical and Neural Engineering, Reykjavík, Iceland; University Hospital of Naples 'Federico II', Department of Advanced Biomedical Sciences, Naples. carloricciardi.93@gmail.com. 3Reykjavík University, Institute for Biomedical and Neural Engineering, Reykjavík. dcrjacob@gmail.com. 4University of Iceland, Faculty of Medicine, Reykjavík, Iceland; Landspítali Hospital, Orthopaedic Clinic, Reykjavík. halldor@landspitali.is. 5Umberto I Hospital, Local Health Unit of Salerno, Salerno. m.gambacorta@aslsalerno.it. 6University Hospital of Naples "Federico II", Department of Public Health, Naples. ing.improta@gmail.com. 7Reykjavík University, Institute for Biomedical and Neural Engineering, Reykjavík, Iceland; Landspítali Hospital, Department of Science, Reykjavík. paologar@landspitali.is. (PagePress, 2021-03-09)
      The purpose of this study is to use Health Technology Assessment (HTA) through the Six Sigma (SS) and DMAIC (Define, Measure, Analyse, Improve, Control) problem-solving strategies for comparing cemented and uncemented prostheses in terms of the costs incurred for Total hip arthroplasty (THA) and the length of hospital stay (LOS). Multinomial logistic regression analysis for modelling the data was also performed. Quantitative parameters extracted from gait analysis, electromyography and computed tomography images were used to compare the approaches, but the analysis did not show statistical significance. The variables regarding costs were studied with the Mann-Whitney and Kruskal-Wallis tests. No statistically significant difference between cemented and uncemented prosthesis for the total cost of LOS was found, but the cost of the surgeon had an influence on the overall expenses, affecting the cemented prosthetic approach. The material costs of surgery for the uncemented prosthesis and the cost of theatre of surgery for the cemented prosthesis were the most influential. Multinomial logistic regression identified the Vastus Lateralis variable as statistically significant. The overall accuracy of the model is 93.0%. The use of SS and DMAIC cycle as tools of HTA proved that the cemented and uncemented approaches for THA have similar costs and LOSy.
    • Improving Prosthetic Selection and Predicting BMD from Biometric Measurements in Patients Receiving Total Hip Arthroplasty.

      Ricciardi, Carlo; Jónsson, Halldór; Jacob, Deborah; Improta, Giovanni; Recenti, Marco; Gíslason, Magnús Kjartan; Cesarelli, Giuseppe; Esposito, Luca; Minutolo, Vincenzo; Bifulco, Paolo; et al. (MDPI, 2020-10-14)
      There are two surgical approaches to performing total hip arthroplasty (THA): a cemented or uncemented type of prosthesis. The choice is usually based on the experience of the orthopaedic surgeon and on parameters such as the age and gender of the patient. Using machine learning (ML) techniques on quantitative biomechanical and bone quality data extracted from computed tomography, electromyography and gait analysis, the aim of this paper was, firstly, to help clinicians use patient-specific biomarkers from diagnostic exams in the prosthetic decision-making process. The second aim was to evaluate patient long-term outcomes by predicting the bone mineral density (BMD) of the proximal and distal parts of the femur using advanced image processing analysis techniques and ML. The ML analyses were performed on diagnostic patient data extracted from a national database of 51 THA patients using the Knime analytics platform. The classification analysis achieved 93% accuracy in choosing the type of prosthesis; the regression analysis on the BMD data showed a coefficient of determination of about 0.6. The start and stop of the electromyographic signals were identified as the best predictors. This study shows a patient-specific approach could be helpful in the decision-making process and provide clinicians with information regarding the follow up of patients. Keywords: clinical decision making; database analyses; electromyography; machine learning; total hip arthroplasty.
    • P300 Analysis Using High-Density EEG to Decipher Neural Response to rTMS in Patients With Schizophrenia and Auditory Verbal Hallucinations.

      Aubonnet, Romain; Banea, Ovidiu C; Sirica, Roberta; Wassermann, Eric M; Yassine, Sahar; Jacob, Deborah; Magnúsdóttir, Brynja Björk; Haraldsson, Magnús; Stefansson, Sigurjon B; Jónasson, Viktor D; et al. (Frontiers Research Foundation, 2020-11-20)
      Schizophrenia is a complex disorder about which much is still unknown. Potential treatments, such as transcranial magnetic stimulation (TMS), have not been exploited, in part because of the variability in behavioral response. This can be overcome with the use of response biomarkers. It has been however shown that repetitive transcranial magnetic stimulation (rTMS) can the relieve positive and negative symptoms of schizophrenia, particularly auditory verbal hallucinations (AVH). This exploratory work aims to establish a quantitative methodological tool, based on high-density electroencephalogram (HD-EEG) data analysis, to assess the effect of rTMS on patients with schizophrenia and AVH. Ten schizophrenia patients with drug-resistant AVH were divided into two groups: the treatment group (TG) received 1 Hz rTMS treatment during 10 daily sessions (900 pulses/session) over the left T3-P3 International 10-20 location. The control group (CG) received rTMS treatment over the Cz (vertex) EEG location. We used the P300 oddball auditory paradigm, known for its reduced amplitude in schizophrenia with AVH, and recorded high-density electroencephalography (HD-EEG, 256 channels), twice for each patient: pre-rTMS and 1 week post-rTMS treatment. The use of HD-EEG enabled the analysis of the data in the time domain, but also in the frequency and source-space connectivity domains. The HD-EEG data were linked with the clinical outcome derived from the auditory hallucinations subscale (AHS) of the Psychotic Symptom Rating Scale (PSYRATS), the Quality of Life Scale (QoLS), and the Depression, Anxiety and Stress Scale (DASS). The general results show a variability between subjects, independent of the group they belong to. The time domain showed a higher N1-P3 amplitude post-rTMS, the frequency domain a higher power spectral density (PSD) in the alpha and beta bands, and the connectivity analysis revealed a higher brain network integration (quantified using the participation coefficient) in the beta band. Despite the small number of subjects and the high variability of the results, this work shows a robust data analysis and an interplay between morphology, spectral, and connectivity data. The identification of a trend post-rTMS for each domain in our results is a first step toward the definition of quantitative neurophysiological parameters to assess rTMS treatment. Keywords: P300; TMS (repetitive transcranial magnetic stimulation); brain connectivity; high-density EEG; schizophrenia; spectral analysis; temporal analysis.
    • Predicting body mass index and isometric leg strength using soft tissue distributions from computed tomography scans

      Recenti, Marco; Ricciardi, Carlo; Monet, Anaïs; Jacob, Deborah; Ramos, Jorgelina; Gìslason, Magnus; Edmunds, Kyle; Carraro, Ugo; Gargiulo, Paolo; aInstitute of Biomedical and Neural Engineering, Reykjavik University, Menntavegi 1, Reykjavik, 102, Iceland bDepartment of Advanced Biomedical Sciences, University Hospital of Naples ‘Federico II’, Via Sergio Pansini 5, Naples, 80131, Italy cCIR-Myo, Department of Biomedical Sciences, University of Padova, Via Ugo Bassi 58/B, Padova, 35121, Italy dDepartment of Science, Landspitali, Hingbraut, Reykjavik, 101, Iceland (SPRINGER HEIDELBERG, 2020-01-01)
      This paper describes the interconnections and predictive value between Body Mass Index (BMI), Isometric Leg Strength (ISO) and soft tissue distribution from mid-thigh Computed Tomography (CT) scans using Machine Learning (ML) regression and classification algorithms. A novel methodology for soft tissue patient specific CT profile called Nonlinear Trimodal Regression Analysis (NTRA) was developed using radiodensitomentric distribution from a CT scan. This method defines 11 parameters used as input features for Tree-Based ML algorithms in order to apply regression and classification on BMI and ISO. K_fold Cross-Validation with k = 10 is applied to obtain several models to choose the best one using the higher coefficient of determination (R-2) as an evaluator of the quality of regression prediction. Following this, BMI and ISO are divided into 3 and 5 classes and the same methodology is used to classify them. For this analysis, an accuracy parameter is calculated to evaluate the quality of the results. The max R-2 is 88.9 for the BMI and it is obtained using the Gradient-Boosting Algorithm. The best accuracy was 76.1 for 3 classes and 73.1 for 5 classes. The best results obtained for ISO are R-2 = 66.5 and an accuracy of 65.5 for the 3 classes classification. Furthermore, the connective tissue assumes high importance in the prediction process. In this methodological study the feasibility of a ML approach was tested with good results, in order to show a novel approach to study the correlation between physiology parameters and imaging.
    • Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals.

      Recenti, Marco; Ricciardi, Carlo; Aubonnet, Romain; Picone, Ilaria; Jacob, Deborah; Svansson, Halldór Á R; Agnarsdóttir, Sólveig; Karlsson, Gunnar H; Baeringsdóttir, Valdís; Petersen, Hannes; et al. (Frontiers Media, 2021-04-01)
      Motion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (I MS ). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for I MS ). The feature importance analysis showed that muscle parameters are the most relevant, and for EEG analysis, beta wave results were the most important. The present work serves as the first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in the evaluation of PC disruptions, which are among the most disturbing and costly health conditions affecting humans.