Detection of squawks in respiratory sounds of mechanically ventilated COVID-19 patients

Mechanically ventilated patients typically exhibit abnormal respiratory sounds. Squawks are short inspiratory adventitious sounds that may occur in patients with pneumonia, such as COVID-19 patients. In this work we devised a method for squawk detection in mechanically ventilated patients by developing algorithms for respiratory cycle estimation, squawk candidate identification, feature extraction, and clustering. The best classifier reached an F1 of 0.48 at the sound file level and an F1 of 0.66 at the recording session level. These preliminary results are promising, as they were obtained in noisy environments. This method will give health professionals a new feature to assess the potential deterioration of critically ill patients.publishe

Clinical validation of an algorithm for rapid and accurate automated segmentation of intracoronary optical coherence tomography images

Objectives: The analysis of intracoronary optical coherence tomography (OCT) images is based on manual identification of the lumen contours and relevant structures. However, manual image segmentation is a cumbersome and time-consuming process, subject to significant intra- and inter-observer variability. This study aims to present and validate a fully-automated method for segmentation of intracoronary OCT images. Methods: We studied 20 coronary arteries (mean length = 39.7 ± 10.0 mm) from 20 patients who underwent a clinically-indicated cardiac catheterization. The OCT images (n = 1812) were segmented manually, as well as with a fully-automated approach. A semi-automated variation of the fully-automated algorithm was also applied. Using certain lumen size and lumen shape characteristics, the fully- and semi-automated segmentation algorithms were validated over manual segmentation, which was considered as the gold standard. Results: Linear regression and Bland–Altman analysis demonstrated that both the fully-automated and semiautomated segmentation had a very high agreement with the manual segmentation, with the semi-automated approach being slightly more accurate than the fully-automated method. The fully-automated and semiautomated OCT segmentation reduced the analysis time by more than 97% and 86%, respectively, compared to manual segmentation. Conclusions: In the current work we validated a fully-automated OCT segmentation algorithm, as well as a semiautomated variation of it in an extensive “real-life” dataset of OCT images. The study showed that our algorithm can perform rapid and reliable segmentation of OCT images

Accurate and reproducible reconstruction of coronary arteries and endothelial shear stress calculation using 3D OCT: Comparative study to 3D IVUS and 3D QCA

Background: Geometrically-correct 3D OCT is a new imaging modality with the potential to investigate the association of local hemodynamic microenvironment with OCT-derived high-risk features. We aimed to describe the methodology of 3D OCT and investigate the accuracy, inter- and intra-observer agreement of 3D OCT in reconstructing coronary arteries and calculating ESS, using 3D IVUS and 3D QCA as references. Methods-Results: 35 coronary artery segments derived from 30 patients were reconstructed in 3D space using 3D OCT. 3D OCT was validated against 3D IVUS and 3D QCA. The agreement in artery reconstruction among 3D OCT, 3D IVUS and 3D QCA was assessed in 3-mm-long subsegments using lumen morphometry and ESS parameters. The inter- and intra-observer agreement of 3D OCT, 3D IVUS and 3D QCA were assessed in a representative sample of 61 subsegments (n ¼ 5 arteries). The data processing times for each reconstruction methodology were also calculated. There was a very high agreement between 3D OCT vs. 3D IVUS and 3D OCT vs. 3D QCA in terms of total reconstructed artery length and volume, as well as in terms of segmental morphometric and ESS metrics with mean differences close to zero and narrow limits of agreement (BlandeAltman analysis). 3D OCT exhibited excellent inter- and intra-observer agreement. The analysis time with 3D OCT was significantly lower compared to 3D IVUS. Conclusions: Geometrically-correct 3D OCT is a feasible, accurate and reproducible 3D reconstruction technique that can perform reliable ESS calculations in coronary arteries

Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation Coefficients

Intravascular Optical Coherence Tomography (IVOCT) images provide important insight into every aspect of atherosclerosis. Specifically, the extent of plaque and its type, which are indicative of the patient’s condition, are better assessed by OCT images in comparison to other in vivo modalities. A large amount of imaging data per patient require automatic methods for rapid results. An effective step towards automatic plaque detection and plaque characterization is axial lines (A-lines) based classification into normal and various plaque types. In this work, a novel automatic method for A-line classification is proposed. The method employed convolutional neural networks (CNNs) for classification in its core and comprised the following pre-processing steps: arterial wall segmentation and an OCT-specific (depth-resolved) transformation and a post-processing step based on the majority of classifications. The important step was the OCT-specific transformation, which was based on the estimation of the attenuation coefficient in every pixel of the OCT image. The dataset used for training and testing consisted of 183 images from 33 patients. In these images, four different plaque types were delineated. The method was evaluated by cross-validation. The mean values of accuracy, sensitivity and specificity were 74.73%, 87.78%, and 61.45%, respectively, when classifying into plaque and normal A-lines. When plaque A-lines were classified into fibrolipidic and fibrocalcific, the overall accuracy was 83.47% for A-lines of OCT-specific transformed images and 74.94% for A-lines of original images. This large improvement in accuracy indicates the advantage of using attenuation coefficients when characterizing plaque types. The proposed automatic deep-learning pipeline constitutes a positive contribution to the accurate classification of A-lines in intravascular OCT images

Analysis of medical data from multiple imaging modalities with machine learning techniques, image processing and statistical shape models

This thesis describes innovate methods of image processing on data having been collected through various medical imaging systems (OCT and SPECT / CT) and related to specific issues - problems of the diagnostic medicine. These methods were developed in accordance with the principles of image processing, by using convolutional neural networks and active shape models. We examined basically images of Intravascular Optical Coherence Tomography (which constitutes a relatively new and evolving medical image technology), in view of the morphology and pathophysiology of the vascular arteries, and with the aim of developing methods for automatic segmentation of the arterial wall and classification of atherosclerotic tissues. We developed the ARC-OCT method which enables accurate and fully automatic detection of lumen-endothelial borders even in OCT images containing artifacts, arterial stented segments and lateral branches. For the automatic classification of atherosclerotic plaques, (after having apply - test a variety of alternative techniques) we developed a combinatorial method based on the specialized pre-treatment of OCT images (to highlight the optical properties of the different types of atherosclerotic tissues) and classification of them in two level bases: textural and regional, using the appropriate CNNs network at each level. The method was used to classify portions of an IVOCT image into 6 categories: background, normal tissue, and four different types of atherosclerotic plaque (lipid, fibrous, calcified and mixed). Furthermore, for the automatic segmentation of V/P SPECT functional images of the lungs - another field of our research work- we developed a method which is based on Active Shape Models, that is the transferring the a priori information, derived from the corresponding anatomical CT, to the segmentation procedure. The algorithm that was developed allows estimating the contour of the lungs independently of the existence of peripheral ventilation / perfusion defects and can form the basis for the automatic quantification of large-scale of relevant measurements. Finally, we identified issues (on the above fields) that require further research and development, that can be done by using deep learning methods and 3D representation techniques, so that the results of our research effort can be more widely applied in clinical practice.Η παρούσα διατριβή περιγράφει νέες πρωτότυπες μεθόδους επεξεργασίας ιατρικών απεικονιστικών δεδομένων που συλλέχθηκαν με την εφαρμογή διαφόρων συστημάτων απεικόνισης (OCT και SPECT/CT) και σχετίζονται με συγκεκριμένα θέματα - προβλήματα της διαγνωστικής ιατρικής. Οι μέθοδοι αυτές αναπτύχτηκαν σύμφωνα με τις αρχές της επεξεργασίας εικόνας, με τη χρήση συνελικτικών νευρωνικών δικτύων και ενεργού μοντέλου σχήματος. Εξετάστηκαν, κατά βάση, εικόνες της Ενδοστεφανιαίας Οπτικής Συνεκτικής Τομογραφίας (νέας σχετικά και εξελισσόμενης απεικονιστικής τεχνολογίας) με στόχο την ανάπτυξη μεθόδων αυτόματης κατάτμησης του αρτηριακού τοιχώματος και κατηγοριοποίησης των αθηρωματικών ιστών (διερεύνηση της μορφολογίας και παθολογοανατομίας των στεφανιαίων αρτηριών). Η μέθοδος ARC-OCT που αναπτύχθηκε καθιστά δυνατή την ακριβή και πλήρως αυτοματοποιημένη ανίχνευση του ορίου αυλού-ενδοθηλίου, ακόμη και σε εικόνες OCT με τεχνικά σφάλματα και επί τμημάτων στεφανιαίων αρτηριών που φέρουν ενδοπροθέσεις ή έχουν πλευρικές διακλαδώσεις. Για την αυτόματη κατηγοριοποίηση των αθηρωματικών πλακών (μετά από την εφαρμογή - δοκιμή πληθώρας εναλλακτικών τεχνικών), αναπτύχθηκε μια συνδυαστική μέθοδος που βασίζεται στην εξειδικευμένη προεπεξεργασία των εικόνων OCT (για την ανάδειξη των οπτικών ιδιοτήτων των διαφόρων τύπων αθηρωματικών ιστών) και την ταξινόμησή τους σε δύο επίπεδα: τόσο υφής, όσο και περιοχής, με τη χρήση του κατάλληλου συνελικτικού νευρωνικού δικτύου CNN, σε κάθε επίπεδο. Με την μέθοδο επιτεύχθηκε η κατάταξη των τμημάτων μιας εικόνας IVOCT, σε 6 κατηγορίες: υπόβαθρο εικόνας, φυσιολογικός ιστός και τέσσερις διαφορετικούς τύπους αθηρωματικής πλάκας (λιπώδης, ινώδης, ασβεστοποιημένη και μεικτή). Ακόμη, για την αυτόματη κατάτμηση-οριοθέτηση των λειτουργικών εικόνων αερισμού/αιμάτωσης SPECT πνευμόνων (που απετέλεσε ένα ακόμη από τα πεδία της παρούσας εργασίας), η μέθοδος που αναπτύχθηκε βασίζεται στην αξιοποίηση των a priori πληροφοριών ενεργών προτύπων σχημάτων που προκύπτουν από τις αντίστοιχες ανατομικές εικόνες CT. Ο αλγόριθμος που δημιουργήθηκε επιτρέπει την εκτίμηση του περιγράμματος των πνευμόνων ανεξάρτητα από την ύπαρξη περιφερειακών ελαττωμάτων αερισμού /αιμάτωσης και μπορεί να αποτελέσει τη βάση προς την κατεύθυνση της αυτόματης ποσοτικοποίησης μεγάλης κλίμακας σχετικών μετρήσεων. Τέλος προσδιορίστηκαν τα θέματα (επί των παραπάνω πεδίων) που χρειάζονται περαιτέρω έρευνα - ανάπτυξη, που μπορεί να γίνει με την αξιοποίηση των μεθόδων βαθιάς μάθησης και τη χρήση τεχνικών 3D αναπαράστασης, με στόχο τα πορίσματα αυτής της ερευνητικής προσπάθειας να έχουν ευρύτερη εφαρμογή στην κλινική πράξη

A Study of Machine Learning Regression Techniques for Non-Contact SpO₂ Estimation from Infrared Motion-Magnified Facial Video

This work explores the use of infrared low-cost cameras for monitoring peripheral oxygen saturation (SpO2), a vital sign that is particularly important for individuals with fragile health, such as the elderly. The development of contactless SpO2 monitoring utilizing RGB cameras has already proven successful. This study utilizes the Eulerian Video Magnification (EVM) technique to enhance minor variations in skin pixel intensity in particular facial regions. More specifically, the emphasis in this study is in the utilization of infrared cameras, in order to explore the possibility of contactless SpO2 monitoring under low-light or night-time conditions. Many different methods were employed for regression. A study of machine learning regression methods was performed, including a Generalized Additive Model (GAM) and an Extra Trees Regressor, based on 12 novel features extracted from the extracted amplified photoplethysmography (PPG) signal. Deep learning methods were also explored, including a 3D Convolution Neural Network (CNN) and a Video Vision Transformer (ViViT) architecture on the amplified forehead/cheeks video. The estimated SpO2 values of the best performing method reach a low root mean squared error of 1.331 and an R2 score of 0.465 that fall within the acceptable range for these applications

Deep Learning Method to Detect Plaques in IVOCT Images

Intravascular Optical Coherence Tomography (IVOCT) is a modality which gives in vivo insight of coronaries’ artery morphology. Thus, it helps diagnosis and prevention of atherosclerosis. About 100-300 cross-sectional OCT images are obtained for each artery. Therefore, it is important to facilitate and objectify the process of detecting regions of interest, which otherwise demand a lot of time and effort from medical experts. We propose a processing pipeline to automatically detect parts of the arterial wall which are not normal and possibly consist of plaque. The first step of the processing is transforming OCT images to polar coordinates and to detect the arterial wall. After binarization of the image and removal of the catheter, the arterial wall is detected in each axial line from the first white pixel to a depth of 80 pixels which is equal to 1.5 mm. Then, the arterial wall is split to orthogonal patches which undergo OCT-specific transformations and are labelled as plaque (4 distinct kinds: fibrous, calcified, lipid and mixed) or normal tissue. OCT-specific transformations include enhancing the more reflective parts of the image and rendering patches independent of the arterial wall curvature. The patches are input to AlexNet which is fine-tuned to learn to classify them. Fine-tuning is performed by retraining an already trained AlexNet with a learning rate which is 20 times larger for the last 3 fully-connected layers than for the initial 5 convolutional layers. 114 cross-sectional images were randomly selected to fine-tune AlexNet while 6 were selected to validate the results. Training accuracy was 100% while validation accuracy was 86%. Drop in validation accuracy rate is attributed mainly to false negatives which concern only calcified plaque. Thus, there is potential in this method especially in detecting the 3 other classes of plaque

Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function

Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient's quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consuming process that requires high medical expertise. The capabilities that deep learning offers could be exploited in order that robust lung sound classification models can be designed. In this paper, we propose a novel hybrid neural model that implements the focal loss (FL) function to deal with training data imbalance. Features initially extracted from short-time Fourier transform (STFT) spectrograms via a convolutional neural network (CNN) are given as input to a long short-term memory (LSTM) network that memorizes the temporal dependencies between data and classifies four types of lung sounds, including normal, crackles, wheezes, and both crackles and wheezes. The model was trained and tested on the ICBHI 2017 Respiratory Sound Database and achieved state-of-the-art results using three different data splitting strategies-namely, sensitivity 47.37%, specificity 82.46%, score 64.92% and accuracy 73.69% for the official 60/40 split, sensitivity 52.78%, specificity 84.26%, score 68.52% and accuracy 76.39% using interpatient 10-fold cross validation, and sensitivity 60.29% and accuracy 74.57% using leave-one-out cross validation

Ensemble deep learning model for dimensionless respiratory airflow estimation using respiratory sound

In recent years, computerized methods for analyzing respiratory function have gained increased attention within the scientific community. This study proposes a deep-learning model to estimate the dimensionless respiratory airflow using only respiratory sound without prior calibration. We developed hybrid deep learning models (CNN + LSTM) to extract features from the respiratory sound and model their temporal dependencies. Then, we used an ensemble approach to combine multiple outputs of our models and obtain the respiratory airflow waveform for entire respiratory audio signals as the final output. We conducted a comprehensive set of experiments and evaluated the models using several regression evaluation metrics to assess how the models would perform in various circumstances of different complexity. The methods were developed and evaluated considering respiratory sound and electrical impedance tomography (EIT) data from 50 respiratory patients (15 female and 35 male with an average age of 67.4 ± 8.9 years and body mass index of 27.8 ± 5.6 ). An external assessment was conducted using an external database, the Respiratory Sound Database (RSD). This was an indirect evaluation because the RSD does not provide the ground truth values of the dimensionless respiratory airflow. In the most complex evaluation task (Task II), we achieved the following results for the estimation of the normalized dimensionless respiratory airflow curve: mean absolute error = 0.134 ± 0.061; root mean squared error = 0.170 ± 0.075; dynamic time warping similarity = 3.282 ± 1.514; Pearson correlation coefficient = 0.770 ± 0.235. External assessment with the RSD showed that the performance of our model decreased when devices different from the ones used for their training were considered. Our study demonstrated that deep learning models could reliably estimate the dimensionless respiratory airflow.publishe

Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT

Objective: Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. Methods: A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images. Results: The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p < 0.001) in SPECT. There were similar observations when comparing reference volumes from CT and manual delineations in SPECT images, left lung (bias was − 10 ± 491, R = 0.60, p = 0.005) right lung (bias 36 ± 524 ml, R = 0.62, p = 0.004). Conclusion: Automated segmentation on SPECT images are on par with manual segmentation on SPECT images. Relative large volumetric differences between manual delineations of functional SPECT images and anatomical CT images confirms that lung segmentation of functional SPECT images is a challenging task. The current algorithm is a first step towards automatic quantification of wide range of measurements