Active Learning in Physics: From 101, to Progress, and Perspective

Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled samples for training, AL iteratively selects unlabeled samples to be annotated by an expert. This protocol aims to prioritize the most informative samples, leading to improved model performance compared to training with all labeled samples. In recent years, AL has gained increasing attention, particularly in the field of physics. This paper presents a comprehensive and accessible introduction to the theory of AL reviewing the latest advancements across various domains. Additionally, we explore the potential integration of AL with quantum ML, envisioning a synergistic fusion of these two fields rather than viewing AL as a mere extension of classical ML into the quantum realm.Comment: 15 page

Classification model based on strain measurements to identify patients with arrhythmogenic cardiomyopathy with left ventricular involvement

[EN] Background and objective: A heterogenous expression characterizes arrhythmogenic cardiomyopathy (AC). The evaluation of regional wall movement included in the current Task Force Criteria is only qualitative and restricted to the right ventricle. However, a strain-based approach could precisely quantify myocardial deformation in both ventricles. We aim to define and modelize the strain behavior of the left ventricle in AC patients with left ventricular (LV) involvement by applying algorithms such as Principal Component Analysis (PCA), clustering and naive Bayes (NB) classifiers. Methods: Thirty-six AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine cardiac magnetic resonance imaging to assess strain time series from a 3D approach, to which PCA was applied. A Two-Step clustering algorithm separated the patients' group into clusters according to their level of LV strain impairment. A statistical characterization between controls and the new AC subgroups was done. Finally, a NB classifier was built and new data from a small evolutive dataset was predicted. Results: 60% of AC-LV patients showed mildly affected strain and 40% severely affected strain. Both groups and controls exhibited statistically significant differences, especially when comparing controls and severely affected AC-LV patients. The classification accuracy of the strain NB classifier reached 82.76%. The model performance was as good as to classify the individuals with a 100% sensitivity and specificity for severely impaired strain patients, 85.7% and 81.1% for mildly impaired strain patients, and 69.9% and 91.4% for normal strain, respectively. Even when the severely affected LV-AC group was excluded, LV strain showed a good accuracy to differentiate patients and controls. The prediction of the evolutive dataset revealed a progressive alteration of strain in time. Conclusions: Our LV strain classification model may help to identify AC patients with LV involvement, at least in a setting of a high pretest probability, such as family screening.This work was supported by grants from the "Ministerio de Economia y Competitividad"[DPI2015-70821-R], "Instituto de Salud Carlos III " and FEDER "Union Europea, Una forma de hacer Europa"[PI14/01477, PI15/00748, PI18/01582, CIBERCV] and La Fe Biobank [PT17/0 015/0043].Vives-Gilabert, Y.; Zorio, E.; Sanz-Sánchez, J.; Calvillo-Batllés, P.; Millet Roig, J.; Castells, F. (2020). Classification model based on strain measurements to identify patients with arrhythmogenic cardiomyopathy with left ventricular involvement. Computer Methods and Programs in Biomedicine. 188:1-9. https://doi.org/10.1016/j.cmpb.2019.105296S19188Bielza, C., & Larrañaga, P. (2014). Discrete Bayesian Network Classifiers. ACM Computing Surveys, 47(1), 1-43. doi:10.1145/2576868Bourfiss, M., Vigneault, D. M., Aliyari Ghasebeh, M., Murray, B., James, C. A., Tichnell, C., … te Riele, A. S. J. M. (2017). Feature tracking CMR reveals abnormal strain in preclinical arrhythmogenic right ventricular dysplasia/ cardiomyopathy: a multisoftware feasibility and clinical implementation study. Journal of Cardiovascular Magnetic Resonance, 19(1). doi:10.1186/s12968-017-0380-4Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Castells, F., Laguna, P., Sörnmo, L., Bollmann, A., & Roig, J. M. (2007). Principal Component Analysis in ECG Signal Processing. EURASIP Journal on Advances in Signal Processing, 2007(1). doi:10.1155/2007/74580Cevenini, G., Barbini, E., Massai, M. R., & Barbini, P. (2011). A naïve Bayes classifier for planning transfusion requirements in heart surgery. Journal of Evaluation in Clinical Practice, 19(1), 25-29. doi:10.1111/j.1365-2753.2011.01762.xIgual, B., Zorio, E., Maceira, A., Estornell, J., Lopez-Lereu, M. P., Monmeneu, J. V., … Salvador, A. (2011). Resonancia magnética cardiaca en miocardiopatía arritmogénica. Tipos de afección y patrones de realce tardío de gadolinio. Revista Española de Cardiología, 64(12), 1114-1122. doi:10.1016/j.recesp.2011.07.014Marcus, F. I., McKenna, W. J., Sherrill, D., Basso, C., Bauce, B., Bluemke, D. A., … Zareba, W. (2010). Diagnosis of arrhythmogenic right ventricular cardiomyopathy/dysplasia: Proposed Modification of the Task Force Criteria. European Heart Journal, 31(7), 806-814. doi:10.1093/eurheartj/ehq025McKenna, W. J., Thiene, G., Nava, A., Fontaliran, F., Blomstrom-Lundqvist, C., Fontaine, G., & Camerini, F. (1994). Diagnosis of arrhythmogenic right ventricular dysplasia/cardiomyopathy. Task Force of the Working Group Myocardial and Pericardial Disease of the European Society of Cardiology and of the Scientific Council on Cardiomyopathies of the International Society and Federation of Cardiology. Heart, 71(3), 215-218. doi:10.1136/hrt.71.3.215Morales, D. A., Vives-Gilabert, Y., Gómez-Ansón, B., Bengoetxea, E., Larrañaga, P., Bielza, C., … Delfino, M. (2013). Predicting dementia development in Parkinson’s disease using Bayesian network classifiers. Psychiatry Research: Neuroimaging, 213(2), 92-98. doi:10.1016/j.pscychresns.2012.06.001Narula, S., Shameer, K., Salem Omar, A. M., Dudley, J. T., & Sengupta, P. P. (2016). Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. Journal of the American College of Cardiology, 68(21), 2287-2295. doi:10.1016/j.jacc.2016.08.062Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572. doi:10.1080/14786440109462720Prati, G., Vitrella, G., Allocca, G., Muser, D., Buttignoni, S. C., Piccoli, G., … Nucifora, G. (2015). Right Ventricular Strain and Dyssynchrony Assessment in Arrhythmogenic Right Ventricular Cardiomyopathy. Circulation: Cardiovascular Imaging, 8(11). doi:10.1161/circimaging.115.003647Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65. doi:10.1016/0377-0427(87)90125-7Sen-Chowdhry, S., Syrris, P., Ward, D., Asimaki, A., Sevdalis, E., & McKenna, W. J. (2007). Clinical and Genetic Characterization of Families With Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy Provides Novel Insights Into Patterns of Disease Expression. Circulation, 115(13), 1710-1720. doi:10.1161/circulationaha.106.660241Sen-Chowdhry, S., Syrris, P., Prasad, S. K., Hughes, S. E., Merrifield, R., Ward, D., … McKenna, W. J. (2008). Left-Dominant Arrhythmogenic Cardiomyopathy. Journal of the American College of Cardiology, 52(25), 2175-2187. doi:10.1016/j.jacc.2008.09.019Sen-Chowdhry, S., Morgan, R. D., Chambers, J. C., & McKenna, W. J. (2010). Arrhythmogenic Cardiomyopathy: Etiology, Diagnosis, and Treatment. Annual Review of Medicine, 61(1), 233-253. doi:10.1146/annurev.med.052208.130419Sengupta, P. P., Huang, Y.-M., Bansal, M., Ashrafi, A., Fisher, M., Shameer, K., … Dudley, J. T. (2016). Cognitive Machine-Learning Algorithm for Cardiac Imaging. Circulation: Cardiovascular Imaging, 9(6). doi:10.1161/circimaging.115.004330Smiseth, O. A., Torp, H., Opdahl, A., Haugaa, K. H., & Urheim, S. (2015). Myocardial strain imaging: how useful is it in clinical decision making? European Heart Journal, 37(15), 1196-1207. doi:10.1093/eurheartj/ehv529Tabassian, M., Alessandrini, M., Herbots, L., Mirea, O., Pagourelias, E. D., Jasaityte, R., … D’hooge, J. (2017). Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification. The International Journal of Cardiovascular Imaging, 33(8), 1159-1167. doi:10.1007/s10554-017-1108-0Tops, L. F., Prakasa, K., Tandri, H., Dalal, D., Jain, R., Dimaano, V. L., … Abraham, T. P. (2009). Prevalence and Pathophysiologic Attributes of Ventricular Dyssynchrony in Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy. Journal of the American College of Cardiology, 54(5), 445-451. doi:10.1016/j.jacc.2009.04.038Vives-Gilabert, Y., Sanz-Sánchez, J., Molina, P., Cebrián, A., Igual, B., Calvillo-Batllés, P., … Zorio, E. (2019). Left ventricular myocardial dysfunction in arrhythmogenic cardiomyopathy with left ventricular involvement: A door to improving diagnosis. International Journal of Cardiology, 274, 237-244. doi:10.1016/j.ijcard.2018.09.024Wong, K. C. L., Tee, M., Chen, M., Bluemke, D. A., Summers, R. M., & Yao, J. (2016). Regional infarction identification from cardiac CT images: a computer-aided biomechanical approach. International Journal of Computer Assisted Radiology and Surgery, 11(9), 1573-1583. doi:10.1007/s11548-016-1404-

MaRCoS, an open-source electronic control system for low-field MRI

Every magnetic resonance imaging (MRI) device requires an electronic control system that handles pulse sequences and signal detection and processing. Here we provide details on the architecture and performance of MaRCoS, a MAgnetic Resonance COntrol System developed by an open international community of low-field MRI researchers. MaRCoS is inexpensive and can handle cycle-accurate sequences without hard length limitations, rapid bursts of events, and arbitrary waveforms. It can also be easily adapted to meet further specifications required by the various academic and private institutions participating in its development. We describe the MaRCoS hardware, firmware and software that enable all of the above, including a Python-based graphical user interface for pulse sequence implementation, data processing and image reconstruction.Comment: 10 pages, 4 figure

Left ventricular Myocardial dysfunction in arrhythmogenic cardiomyopathy with left ventricular involvement: A door to improving diagnosis.

Background: Diagnostic Task Force Criteria (TFC) for arrhythmogenic cardiomyopathy (AC) exhibit poor performance for left dominant forms. TFC only include right ventricular (RV) dysfunction (akinesia, dyssynchrony, volumes and ejection fraction). Moreover, cardiac magnetic resonance imaging (CMRI) assessment of left ventricular (LV) dyssynchrony has hitherto not been described. Thus, we aimed to comprehensively characterize LV CMRI behavior in AC patients. Methods: Thirty-five AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine CMRI to assess LV ejection fraction (LVEF), LV endsystolic and end-diastolic volume indexes, strain values and dyssynchrony. Regions with more frequent strain and dyssynchrony impairment were also studied. Results: Radial dyssynchrony and LVEF were selected (sensitivities 54.3% and 48.6%, respectively at 100% specificity), with a threshold of 70 ms for radial dyssynchrony and 48.5% for LVEF. 71.4% of patients exceeded these thresholds (31.4% both, 22.9% only dyssynchrony and 17.1% only LVEF). Considering these cut-off values as a novel combined criterion, 30% of patients with 'borderline' or 'possible' AC following 2010 TFC would move to a 'definite' AC diagnosis. Strain was globally impaired whereas dyssynchronous regions were more often apical and located at the inferolateral wall. Conclusions: Mirroring the RV evaluation, we suggest including LVEF and LV dyssynchrony to improve the diagnosis of AC. Two independent mechanisms can be claimed in AC patients with LV involvement: 1) decreased myocardial deformation with global LV affectation and 2) delayed myocardial contraction at localized regions

Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation

We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum circuits comprised of systems with $N_{Q}$ qubits. The primary objective is to utilize physics-inspired deep learning techniques to accurately solve the time evolution of the different physical observables within the quantum system. To accomplish this objective, we embed the necessary physical information into an underlying neural network to effectively tackle the problem. In particular, we impose the hermiticity condition on all physical observables and make use of the principle of least action, guaranteeing the acquisition of the most appropriate counterdiabatic terms based on the underlying physics. The proposed approach offers a dependable alternative to address the CD driving problem, free from the constraints typically encountered in previous methodologies relying on classical numerical approximations. Our method provides a general framework to obtain optimal results from the physical observables relevant to the problem, including the external parameterization in time known as scheduling function, the gauge potential or operator involving the non-adiabatic terms, as well as the temporal evolution of the energy levels of the system, among others. The main applications of this methodology have been the $\mathrm{H_{2}}$ and $\mathrm{LiH}$ molecules, represented by a 2-qubit and 4-qubit systems employing the STO-3G basis. The presented results demonstrate the successful derivation of a desirable decomposition for the non-adiabatic terms, achieved through a linear combination utilizing Pauli operators. This attribute confers significant advantages to its practical implementation within quantum computing algorithms.Comment: 28 pages, 10 figures, 1 algorithm, 1 tabl

Depression and Anxiety Scores Are Associated with Amygdala Volume in Cushing's Syndrome : Preliminary Study

Cushing's syndrome (CS) has repeatedly been associated with hippocampal volume reductions, while little information is available on the amygdala, another structure rich in glucocorticoid receptors. The aim of the study was to analyze amygdala volume in patients with CS and its relationship with anxiety, depression, and hormone levels. 39 CS patients (16 active and 23 patients in remission) and 39 healthy controls matched for age, sex, and education level completed anxiety (STAI) and depression tests (BDI-II) and underwent a 3 Tesla brain MRI and endocrine testing. Amygdala volumes were analysed with FreeSurfer software. Active CS patients had smaller right (but not left) amygdala volumes when compared to controls (P = 0.045). Left amygdala volumes negatively correlated with depression scores (r = −0.692, P = 0.003) and current anxiety state scores (r = −0.617, P = 0.011) in active CS patients and with anxiety trait scores (r = −0.440, P = 0.036) in patients in remission. No correlations were found between current ACTH, urinary free cortisol or blood cortisol levels, and amygdala volumes in either patient group. Patients with active CS have a smaller right amygdala volume in comparison to controls, while left amygdala volumes are associated with mood state in both patient groups

Cardiovascular risk and white matter lesions after endocrine control of Cushing's syndrome

Objective: Cushing's syndrome (CS) is associated with high cardiovascular risk. White matter lesions (WML) are common on brain magnetic resonance imaging (MRI) in patients with increased cardiovascular risk. AIM: To investigate the relationship between cardiovascular risk, WML, neuropsychological performance and brain volume in CS. Design/methods: Thirty-eight patients with CS (23 in remission, 15 active) and 38 controls sex-, age- and education-level matched underwent a neuropsychological and clinical evaluation, blood and urine tests and 3Tesla brain MRI. WML were analysed with the Scheltens scale. Ten-year cardiovascular risk (10CVR) and vascular age (VA) were calculated according to an algorithm based on the Framingham heart study. Results: Patients in remission had a higher degree of WML than controls and active patients (P<0.001 and P=0.008 respectively), which did not correlate with cognitive performance in any group. WML severity positively correlated with diastolic blood pressure (r=0.659, P=0.001) and duration of hypertension (r=0.478, P=0.021) in patients in remission. Both patient groups (active and in remission) had higher 10CVR (P=0.030, P=0.041) and VA than controls (P=0.013, P=0.039). Neither the 10CVR nor the VA correlated with WML, although both negatively correlated with cognitive function and brain volume in patients in remission (P<0.05). Total brain volume and grey matter volume in both CS patient groups were reduced compared to controls (total volume: active P=0.006, in remission P=0.012; grey matter: active P=0.001, in remission P=0.003), with no differences in white matter volume between groups. Conclusions: Patients in remission of Cushing's syndrome (but not active patients) have more severe white matter lesions than controls, positively correlated with diastolic pressure and duration of hypertension. Ten-year cardiovascular risk and vascular age appear to be negatively correlated with the cognitive function and brain volume in patients in remission of Cushing's syndrome

Pattern of Regional Cortical Thinning Associated with Cognitive Deterioration in Parkinson's Disease

Altres ajuts: Sociedad Española de Radiologia Médica (SERAM 06-09)Background: Dementia is a frequent and devastating complication in Parkinson's disease (PD). There is an intensive search for biomarkers that may predict the progression from normal cognition (PD-NC) to dementia (PDD) in PD. Mild cognitive impairment in PD (PD-MCI) seems to represent a transitional state between PD-NC and PDD. Few studies have explored the structural changes that differentiate PD-NC from PD-MCI and PDD patients. Objectives and Methods: We aimed to analyze changes in cortical thickness on 3.0T Magnetic Resonance Imaging (MRI) across stages of cognitive decline in a prospective sample of PD-NC (n = 26), PD-MCI (n = 26) and PDD (n = 20) patients, compared to a group of healthy subjects (HC) (n = 18). Cortical thickness measurements were made using the automatic software Freesurfer. Results: In a sample of 72 PD patients, a pattern of linear and progressive cortical thinning was observed between cognitive groups in cortical areas functionally specialized in declarative memory (entorhinal cortex, anterior temporal pole), semantic knowledge (parahippocampus, fusiform gyrus), and visuoperceptive integration (banks of the superior temporal sulcus, lingual gyrus, cuneus and precuneus). Positive correlation was observed between confrontation naming and thinning in the fusiform gyrus, parahippocampal gyrus and anterior temporal pole; clock copy with thinning of the precuneus, parahippocampal and lingual gyrus; and delayed memory with thinning of the bilateral anteromedial temporal cortex. Conclusions: The pattern of regional decreased cortical thickness that relates to cognitive deterioration is present in PD-MCI patients, involving areas that play a central role in the storage of prior experiences, integration of external perceptions, and semantic processing

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible