Capnographic analysis for disease classification

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 73-76).Existing methods for extracting diagnostic information from carbon dioxide in the exhaled breath are qualitative, through visual inspection, and therefore imprecise. In this thesis, we quantify the CO₂ waveform, or capnogram, in order to discriminate among various lung disorders. Quantitative analyses of the capnogram are conducted by extracting several physiological waveform features and performing classification by discriminant analysis with voting. Our classification methods are tested in distinguishing between records from subjects with normal lung function and patients with cardiorespiratory disease. In a second step, we discriminate between capnograms from patients with obstructive lung disease (chronic obstructive pulmonary disease) and those with restrictive lung disease (congestive heart failure). Our results demonstrate the diagnostic potential of capnography.by Rebecca J. Asher.S.M

Deep Learning with Limited Labels for Medical Imaging

Recent advancements in deep learning-based AI technologies provide an automatic tool to revolutionise medical image computing. Training a deep learning model requires a large amount of labelled data. Acquiring labels for medical images is extremely challenging due to the high cost in terms of both money and time, especially for the pixel-wise segmentation task of volumetric medical scans. However, obtaining unlabelled medical scans is relatively easier compared to acquiring labels for those images. This work addresses the pervasive issue of limited labels in training deep learning models for medical imaging. It begins by exploring different strategies of entropy regularisation in the joint training of labelled and unlabelled data to reduce the time and cost associated with manual labelling for medical image segmentation. Of particular interest are consistency regularisation and pseudo labelling. Specifically, this work proposes a well-calibrated semi-supervised segmentation framework that utilises consistency regularisation on different morphological feature perturbations, representing a significant step towards safer AI in medical imaging. Furthermore, it reformulates pseudo labelling in semi-supervised learning as an Expectation-Maximisation framework. Building upon this new formulation, the work explains the empirical successes of pseudo labelling and introduces a generalisation of the technique, accompanied by variational inference to learn its true posterior distribution. The applications of pseudo labelling in segmentation tasks are also presented. Lastly, this work explores unsupervised deep learning for parameter estimation of diffusion MRI signals, employing a hierarchical variational clustering framework and representation learning

Physiology-based Mathematical Models for the Intensive Care Unit: Application to Mechanical Ventilation

This work takes us a step closer to realizing personalized medicine, complementing empirical and heuristic way in which clinicians typically work. This thesis presents mechanistic models of physiology. These models, given continuous signals from a patient, can be fine-tuned via parameter estimation methods so that the model's outputs match the patient's. We thus obtain a virtual patient mimicking the patient at hand. Therapeutic scenarios can then be applied and optimal diagnosis and therapy can thus be attained. As such, personalized medicine can then be achieved without resorting to costly genetics. In particular we have developed a novel comprehensive mathematical model of the cardiopulmonary system that includes cardiovascular circulation, respiratory mechanics, tissue and alveolar gas exchange, as well as short-term neural control. Validity of the model was proven by the excellent agreement with real patient data, under normo-physiological as well as hypercapnic and hypoxic conditions, taken from literature. As a concrete example, a submodel of the lung mechanics was fine-tuned using real patient data and personalized respiratory parameters (resistance, R_rs, and compliance, C_rs) were estimated continually. This allows us to compute the patient's effort (Work of Breathing), continuously and more importantly noninvasively. Finally, the use of Bayesian estimation techniques, which allow incorporation of population studies and prior information about model's parameters, was proposed in the contest of patient-specific physiological models. A Bayesian Maximum a Posteriori Probability (MAP) estimator was implemented and applied to a case-study of respiratory mechanics. Its superiority against the classical Least Squares method was proven in data-poor conditions using both simulated and real animal data. This thesis can serve as a platform for a plethora of applications for cardiopulmonary personalized medicine

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support

Modeling and Estimation of Cardiorespiratory Function, with Application to Mechanical Ventilation

Evidence-based medicine is at the heart of current medical practice where clinical decisions are driven by research data. However, most current therapy recommendations follow generalized protocols and guidelines that are based on epidemiological (population) studies and thus not suited for the individual patient's demands. Patient-tailored therapies are considered, hence, an unmet clinical need. We believe that mathematical models of the physiology can attend to such a clinical need, because they can be tuned to the individual patient. Such models provide a sound mathematical framework for personalized clinical decisions. In particular, physiological models in medicine can serve the following two purposes: 1) They can be an efficient tool to quantify cardiopulmonary dynamics, conduct virtual clinical/physiological experiments, and investigate the effects of specific treatments. 2) Model-based estimation techniques can assess physiological parameters or variables, which are otherwise impractical or dangerous to measure; they can effectively tune a generic model to become patient-specific, able to mimic the behavior of a particular patient. In this thesis, we propose a series of modifications to a previously developed cardiopulmonary model (CP Model) in order to better replicate heart-lung interaction phenomena that are typically observed under mechanical ventilation, hence allowing for a more accurate analysis of ventilation-induced changes in cardiac function. The response of this modified model is validated with experimental data collected during mechanical ventilation conditions. Further, as an industrial application of mathematical models, we present a patient emulator system that comprises the modified CP Model, a physical ventilator, and a piston-cylinder arrangement that serves as an electrical-to-hydraulic transducer. The modified CP Model then serves as the virtual patient that is being ventilated, where disease conditions can be instilled. Such a system is designed to offer a well-controlled experimental environment for ventilator manufacturers to efficaciously test and compare ventilation modalities and therapies, thereby enhancing their verification and validation manufacturing processes. Finally, we develop a model-based approach to estimate (noninvasively) the function of the cardiovascular system, in terms of cardiac performance (i.e., cardiac output) and the dynamics of the systemic arterial tree (i.e., time constant). With this technique, we envision to provide continuous and real-time bedside monitoring of changes in cardiovascular function, such as those induced by changes in ventilator settings