Analyse automatique de radiographies pulmonaires pour le diagnostic précoce du syndrome de détresse respiratoire aiguë

RÉSUMÉ Le Syndrome de Détresse Respiratoire Aiguë (SDRA) est une maladie pulmonaire qui représente la forme la plus grave de l’insuffisance respiratoire aiguë. Elle consiste en une atteinte inflammatoire aiguë des poumons et se manifeste au niveau de la radiographie pulmonaire sous forme d’opacités bilatérales. Le diagnostic de cette maladie est effectué à partir de données sur l’hypoxémie et de l’analyse de la radiographie pulmonaire. L’interprétation de la radiographie pulmonaire par des experts souffre d’une variabilité inter-observateurs élevée, ce qui peut entraîner un diagnostic tardif. Cela est problématique, car un diagnostic retardé d’un patient atteint du SDRA rend son traitement moins efficace et peut, par conséquent, grever son pronostic. D’où l’intérêt de développer un système d’aide à la décision clinique (SADC) pour aider le médecin à établir un diagnostic précoce de la maladie. Les SADC pour le diagnostic automatique de maladies à partir des radiographies sont devenus des outils très importants. Ils consistent à analyser automatiquement les radiographies pour identifier les anomalies et procurer un deuxième avis diagnostic aux médecins. Même si plusieurs SADC ont été déjà développés, il n’existe aucun SADC pour le diagnostic du SDRA. La difficulté principale est due à la superposition de structures osseuses telle que la cage thoracique, dont la propriété de radio-opacité rend leur apparence très similaire à celle des opacités diffuses liées au SDRA dans la radiographie pulmonaire. Une segmentation préalable des côtes entières est requise dans le but de les exclure de l’analyse et de focaliser sur l’étude des opacités diffuses due au SDRA. Pour pouvoir valider un SADC pour le diagnostic du SDRA, la création d’une base de radiographies pulmonaires diagnostiquées avec précision est aussi indispensable. La première partie de cette thèse propose un SADC original pour le diagnostic du SDRA à partir de radiographies pulmonaires. Comme il n’existe actuellement aucun système dédié pour cette pathologie, il a fallu le construire de novo. Le SADC développé consiste à analyser une radiographie du thorax après soustraction des côtes entières (postérieures et antérieures) pour pouvoir extraire des régions d’intérêt (ROI) intercostales qui se composent de tissus pulmonaires et analyser uniquement ces régions. Des caractéristiques statistiques et spectrales sont extraites pour chaque ROI. Ensuite, une méthode de transformation des caractéristiques est appliquée en utilisant l’analyse discriminante linéaire (Linear Discriminant Analysis). Les ROI sont ensuite classifiées comme normales ou anormales en utilisant un classifieur SVM. Finalement, le pourcentage des ROI anormales est calculé pour chaque cadran (chaque poumon est divisé en deux parties appelées cadrans). Si ce pourcentage est supérieur à 34%, le cadran donné est alors considéré comme touché. Et si au moins un cadran du poumon gauche et un cadran du poumon droit sont touchés, alors la radiographie pulmonaire est considérée comme un cas de SDRA. Le SADC proposé a été évalué en utilisant une base de radiographies pulmonaires diagnostiquées avec consensus entre plusieurs experts et des mesures de performance telles que la sensibilité et la spécificité ont été calculées. Le système automatisé développé pour le diagnostic du SDRA a permis d’obtenir une bonne sensibilité et une bonne spécificité (sensibilité = 90.6% et spécificité = 86.5%). La deuxième partie de cette thèse présente une étude sur la variabilité inter-observateurs pour le diagnostic du SDRA à partir de radiographies pulmonaires en utilisant notre système, soit individuellement, soit comme deuxième avis. Cette étude a été réalisée en calculant le coefficient Kappa, d’abord entre les experts, ensuite en utilisant le système d’analyse développé. Notre système d’analyse automatique a permis d’améliorer le coefficient Kappa et d’obtenir une bonne concordance de diagnostic en l’utilisant individuellement (Kappa = 0.77) ainsi qu’une meilleure concordance ou une concordance presque parfaite de diagnostic en l’utilisant comme deuxième avis (Kappa = 0.79-0.86). La troisième partie de cette thèse est consacrée à une analyse des besoins pour un SADC déployable en clinique. Dans cette étude, nous avons remarqué que peu de SADC pour l’interprétation de radiographies pulmonaires ont été commercialisés. Aussi, nous avons montré que plusieurs facteurs doivent être considérés pour développer un SADC en soins intensifs. Ces facteurs incluent : la segmentation interactive pour l’extraction des régions d’intérêt (ROI) afin d’améliorer la performance; le choix des caractéristiques devrait être basé sur les différents aspects qui caractérisent l’apparence de la pathologie sur les radiographies pulmonaires et devraient être combinées pour atteindre une meilleure performance; et la construction de la base de données pour la validation du système joue un rôle très important dans la performance de tout SADC. Ce dernier facteur implique que la base des radiographies pulmonaires doit être construite avec précaution en considérant les facteurs suivants: le nombre de radiographies normales et anormales à utiliser ainsi que la représentativité de la diversité des anomalies; la méthodologie à utiliser pour élaborer l’interprétation des radiographies pulmonaires; et la qualité des images à utiliser. Finalement, la création de bases de radiographies pulmonaires publiques permettrait de comparer différents SADC et de choisir celui ayant la meilleure performance, et par conséquent celui qui doit être testé en premier en clinique. En conclusion, ce projet propose un SADC pour le diagnostic précoce du SDRA à partir de radiographies pulmonaires. Son évaluation a permis de confirmer qu’il peut être utilisé par les médecins pour fournir un deuxième avis afin d’élaborer un diagnostic plus précis. En perspective, pour utiliser notre système tout au long du traitement d’un patient atteint du SDRA, une fusion multimodale d’images (RX/ CT/ TIE) permettrait de visualiser à la fois l’information fonctionnelle et l’information morphologique, ainsi que de connaître l’état actuel du patient. Ceci donnerait lieu à un suivi clinique plus efficace au chevet du patient, entre autres en choisissant les paramètres optimaux pour la ventilation mécanique.----------ABSTRACT Acute Respiratory Distress Syndrome (ARDS) is a lung disease which represents the most severe form of acute respiratory failure. It consists of an acute inflammation of the lungs and manifests as bilateral opacities in chest radiographs. The diagnosis of this disease is done using the chest X-ray and hypoxemia criteria. However, interpretation of chest X-ray by medical experts suffers from high inter-observer variability, which can lead to a delayed diagnosis. This is problematic because any delay in diagnosing ARDS makes its treatment less effective and may, therefore, burden the patient’s prognosis. Hence, there is a clear clinical motivation to develop a computer-aided diagnosis (CAD) system to help the doctor establish an early chest X-ray diagnosis of the disease. CAD systems using X-rays for automatic diagnosis of diseases have become very important tools. They consist in automatically analyzing radiographs to identify abnormalities and providing a second diagnostic opinion to physicians. Even though several CAD systems have already been developed, there is currently no such system for the diagnosis of ARDS. The main difficulty is due to the superposition of bone structures such as the rib cage, whose radiopacity makes their appearance very similar to that of diffuse opacities associated with ARDS in chest radiographs. A preliminary segmentation of the whole ribs is thereby required in order to exclude them from the analysis and to focus on studying only the diffuse opacities linked with ARDS. To validate a CAD system for diagnosing ARDS, the creation of a database of chest X-rays diagnosed accurately is also essential. The first part of this thesis proposes a novel CAD system for the diagnosis of ARDS from chest radiographs. As there is currently no dedicated system for this disease, it was necessary to build it de novo. The CAD system we developed consists in analyzing a chest radiograph by first subtracting the whole ribs (anterior and posterior) from the image, and subsequently extracting intercostal patches and analyzing these regions of interest only, which are made up of lung tissues. Statistical and spectral features are extracted from each patch. A feature transformation method is then applied using Linear Discriminant Analysis (LDA). The patches are then classified as either normal or abnormal using an SVM classifier. Finally, the rate of abnormal patches is calculated for each quadrant (each lung is divided into two parts called quadrants). If this rate is greater than 34%, the given quadrant is then considered as affected. And if at least one quadrant of the left lung and one of the right lung are affected, then the chest radiograph is considered as an ARDS case. The proposed CAD system was evaluated using a database of chest radiographs diagnosed with consensus among several experts, and performance measurements such as sensitivity and specificity were calculated. The automated system developed for diagnosing ARDS achieved a sensitivity and specificity that are both good (sensitivity = 90.6% and specificity = 86.5%). The second part of this thesis presents a study of the inter-observer variability for the diagnosis of ARDS from chest radiographs using our system either by itself or as providing a second opinion. This study was carried out by calculating the Kappa coefficient, first between the medical experts, then by using the proposed CAD system. Our automatic analysis system improved the Kappa coefficient and showed a good diagnostic agreement when used individually (Kappa = 0.77) and a better diagnostic agreement or an almost perfect agreement diagnosis using it to give a second opinion (kappa = 0.79-0.86). The third part of this thesis is devoted to a requirements analysis for a CAD system to be used in the clinical setting. In this study, we noticed that only a few CAD systems for chest X-ray interpretation are commercially available. Thus, we showed that several factors must be considered when developing a CAD system for use in intensive care. These factors include: interactive segmentation for extracting regions of interest (ROI) to improve performance; the choice of features should be based on the different aspects that characterize the appearance of the pathology in chest X-rays and should be combined to achieve better performance; and the construction of a validation database, which plays a very important role in the performance of any CAD system. The latter implies that the database must be carefully constructed by considering the following factors: the number of normal and abnormal chest X-rays to be used and the representativeness of the diversity of abnormalities, the methodology used to interpret the chest X-rays, and the quality of the images to use. Finally, the creation of public databases of pulmonary radiographs would make it easier to compare different CAD systems and to choose the one that performs best and therefore the one to be tested first in the clinical setting. In conclusion, this project proposes a CAD system for the early diagnosis of ARDS from chest X-rays. Its evaluation allowed us to confirm that it can be used by doctors to provide a second opinion with the aim of elaborating a more accurate diagnosis. In future work, to utilize our system throughout the treatment of an ARDS patient, a multimodal image fusion approach (RX / CT / EIT) would allow the visualization of both functional and morphological information, as well as knowing the patient's current condition. This would give rise to more efficient monitoring at the patient’s bedside, in particular by choosing the optimal settings for mechanical ventilation

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS).

PURPOSE: Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient boosted tree models trained on patient health record data for the early prediction of ARDS. MATERIALS AND METHODS: 9919 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) data base. XGBoost gradient boosted tree models for early ARDS prediction were created using routinely collected clinical variables and numerical representations of radiology reports as inputs. XGBoost models were iteratively trained and validated using 10-fold cross validation. RESULTS: On a hold-out test set, algorithm classifiers attained area under the receiver operating characteristic curve (AUROC) values of 0.905 when tested for the detection of ARDS at onset and 0.827, 0.810, and 0.790 for the prediction of ARDS at 12-, 24-, and 48-h windows prior to onset, respectively. CONCLUSION: Supervised machine learning predictions may help predict patients with ARDS up to 48 h prior to onset

Acute Respiratory Distress Syndrome

Acute Respiratory Distress Syndrome is an inflammatory response resulting from injury to the alveolar-­‐capillary membrane. This injury is caused by a systemic inflammatory response that involves either direct trauma to the lung cells, such as a pneumonia, or indirect, such as sepsis. The inflammatory response that is triggered results in leaky alveolar-­‐capillary beds and infiltration of the lungs (Villar, 2011). This is very common with approximately 150,000 cases annually in the United States and a very high mortality rate of 60,000 deaths per year (Pipeling & Fan, 2010). Despite the high mortality rate, 15-­‐35%, there is no set of guidelines for treatment of this condition, and methods of mechanical ventilation are only supportive (Zaglam, Jouvet, Flechelles, Emeriaud & Cheriet, 2014). The most severe form of ARDS is refractory hypoxemia, a life threatening condition, in which there is not an adequate amount of oxygen delivered to the tissues (Villar & Kacmarek, 2013). With more than 60 causes of ARDS, it is essential for nursing staff working directly with these patients to be aware of signs and early detection allowing more rapid initiation of treatment modalities in the hope of decreasing patient mortality (Taylor, 2005)

Performance Evaluation of the NASNet Convolutional Network in the Automatic Identification of COVID-19

This paper evaluates the performance of the Neural Architecture Search Network (NASNet) in the automatic detection of COVID-19 (Coronavirus Disease 2019) from chest x-ray images. COVID-19 is a disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that produces in patients fever, cough, shortness of breath, muscle pain, sputum production, diarrhea, and even sore throat. The virus spreads through the air, and to date, is expanding as a global pandemic. There is no vaccine, and it is fatal to approximately 2-7% of the infected population. Among the clinical and paraclinical characteristics of infected patients, nodules have been identified in images of chest x-rays that can be visually identified, producing a simple, rapid, and generally available method of identification. However, the rapid spread of the disease means that there is a lack of specialized medical personnel capable of identifying it, which is why automated schemes are being developed. We propose the tuning of a NASNet-type convolutional model to automatically determine the initial state of a patient in the triage process or intervention protocol of health care centers. The neural network is trained with public images of cases positively identified as patients infected with the virus and patients in normal conditions without infection. Performance evaluation is also done with real images unknown to the neuronal model. As for performance metrics, we use the function of loss of cross-entropy (categorical cross-entropy), the accuracy (or success rate), and the MSE (Mean Squared Error). The tuned model was able to correctly classify the test images with an accuracy of 97%

Septic Pulmonary Embolism Requiring Critical Care: Clinicoradiological Spectrum, Causative Pathogens and Outcomes

OBJECTIVES: Septic pulmonary embolism is an uncommon but life-threatening disorder. However, data on patients with septic pulmonary embolism who require critical care have not been well reported. This study elucidated the clinicoradiological spectrum, causative pathogens and outcomes of septic pulmonary embolism in patients requiring critical care. METHODS: The electronic medical records of 20 patients with septic pulmonary embolism who required intensive care unit admission between January 2005 and December 2013 were reviewed. RESULTS: Multiple organ dysfunction syndrome developed in 85% of the patients, and acute respiratory failure was the most common organ failure (75%). The most common computed tomographic findings included a feeding vessel sign (90%), peripheral nodules without cavities (80%) or with cavities (65%), and peripheral wedge-shaped opacities (75%). The most common primary source of infection was liver abscess (40%), followed by pneumonia (25%). The two most frequent causative pathogens were Klebsiella pneumoniae (50%) and Staphylococcus aureus (35%). Compared with survivors, nonsurvivors had significantly higher serum creatinine, arterial partial pressure of carbon dioxide, and Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores, and they were significantly more likely to have acute kidney injury, disseminated intravascular coagulation and lung abscesses. The in-hospital mortality rate was 30%. Pneumonia was the most common cause of death, followed by liver abscess. CONCLUSIONS: Patients with septic pulmonary embolism who require critical care, especially those with pneumonia and liver abscess, are associated with high mortality. Early diagnosis, appropriate antibiotic therapy, surgical intervention and respiratory support are essential

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

A retrospective audit of the clinical value of routine chest radiographs in the first 24 hours after cardiac surgery using medical records

Routine postoperative chest radiography after cardiac surgery is a common practice, although studies, both prospective and retrospective, conducted in their majority outside Africa, have shown that these chest radiographs are of low clinical value, mainly due to limited impact on patient management. Following cardiac surgery and admission to ICU, chest radiographs are obtained in order to ensure proper position of all invasive devices such as endotracheal tubes, invasive catheters as well as nasograstric tubes, and to exclude possibility of a pneumothorax, atelectasis, infiltrates, and other potential respiratory complications associated with ventilatory support. Following cardiac surgery, there are other elements that require assessment: mediastinum (for widening due to bleeding), pleural space (for presence of fluid or air) and cardiovascular system (for presence of signs of failure). Specific to cardiac surgery is the post-operative pulmonary dysfunction (PPD), where systemic inflammatory response due to cardiopulmonary bypass is the main culprit [Milot J et al, 2001] - leading to acute lung injury. Over and above the usual cardiovascular diseases that require surgical intervention, in Sub-Saharan Africa, inflammatory and infective conditions such as pulmonary tuberculosis, pulmonary hydatid disease, and pulmonary complications of HIV infection, are very prevalent. These pre-existing lung pathologies predispose patients to postoperative pulmonary complications after cardiac surgery. This audit investigates the role and importance of bedside chest X-rays in post operative care of cardiac surgery patients that come from a population group where lung pathology is quite prevalent

Label Uncertainty and Learning Using Partially Available Privileged Information for Clinical Decision Support: Applications in Detection of Acute Respiratory Distress Syndrome

Artificial intelligence and machine learning have the potential to transform health care by deriving new and important insights from the vast amount of data generated during routine delivery of healthcare. The digitization of health data provides an important opportunity for new knowledge discovery and improved care delivery through the development of clinical decision support that can leverage this data to support various aspects of healthcare - from early diagnosis to epidemiology, drug development, and robotic-assisted surgery. These diverse efforts share the ultimate goal of improving quality of care and outcome for patients. This thesis aims to tackle long-standing problems in machine learning and healthcare, such as modeling label uncertainty (e.g., from ambiguity in diagnosis or poorly labeled examples) and representation of data that may not be reliably accessible in a live environment. Label uncertainty hinges on the fact that even clinical experts may have low confidence when assigning a medical diagnosis to some patients due to ambiguity in the case or imperfect reliability of the diagnostic criteria. As a result, some data used for machine training may be mislabeled, hindering the model’s ability to learn the complexity of the underlying task and adversely affecting the algorithm’s overall performance. In this work, I describe a heuristic approach for physicians to quantify their diagnostic uncertainty. I also propose an implementation of instance-weighted support vector machines to incorporate this information during model training. To address the issue of unreliable data, this thesis examines the idea of learning using “partially available” privileged information. This paradigm, based on knowledge transfer, allows for models to use additional data available during training but may not be accessible during testing/deployment. This type of data is abundant in healthcare, where much more information about a patient’s health status is available in retrospective analysis (e.g., in the training data) but not available in real-time environments (e.g., in the test set). In this thesis, “privileged information” are features extracted from chest x-rays (CXRs) using novel feature engineering algorithms and transfer learning with deep residual networks. This example works well for numerous clinical applications, since CXRs are retrospectively accessible during model training but may not be available in a live environment due to delay from ordering, developing, and processing the request. This thesis is motivated by improving diagnosis of acute respiratory distress syndrome (ARDS), a life-threatening lung injury associated with high mortality. The diagnosis of ARDS serves as a model for many medical conditions where standard tests are not routinely available and diagnostic uncertainty is common. While this thesis focuses on improving diagnosis of ARDS, the proposed learning methods will generalize across various healthcare settings, allowing for better characterization of patient health status and improving the overall quality of patient care. This thesis also includes development of methods for time-series analysis of longitudinal health data, signal processing techniques for quality assessment, lung segmentation from complex CXRs, and novel feature extraction algorithm for quantification of pulmonary opacification. These algorithms were tested and validated on data obtained from patients at Michigan Medicine and additional external sources. These studies demonstrate that careful, principled use of methodologies in machine learning and artificial intelligence can potentially assist healthcare providers with early detection of ARDS and help make a timely, accurate medical diagnosis to improve outcomes for patients.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167930/1/nreamaro_1.pd