Distinguishing Between Asthma and Pneumonia Through Automated Lung Sound Analysis

This project attempts to distinguish between two pulmonary disorders, asthma and pneumonia, using automated analysis of lung sounds. Such an approach minimizes the subjectivity of diagnosis inherent to current practices by physicians. Breath sounds are recorded by a physiological microphone and hardware acquisition system, and then analyzed in software using a two stage algorithm. The first stage detects abnormal lung sounds and second stage makes a diagnosis. A clinical trial was conducted at a pediatric clinic to validate the system

Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues

The autofluorescence (AF) characteristics of endogenous fluorophores allow the label-free assessment and visualization of cells and tissues of the human body. While AF imaging (AFI) is well-established in ophthalmology, its clinical applications are steadily expanding to other disciplines. This review summarizes clinical advances of AF techniques published during the past decade. A systematic search of the MEDLINE database and Cochrane Library databases was performed to identify clinical AF studies in extra-ophthalmic tissues. In total, 1097 articles were identified, of which 113 from internal medicine, surgery, oral medicine, and dermatology were reviewed. While comparable technological standards exist in diabetology and cardiology, in all other disciplines, comparability between studies is limited due to the number of differing AF techniques and non-standardized imaging and data analysis. Clear evidence was found for skin AF as a surrogate for blood glucose homeostasis or cardiovascular risk grading. In thyroid surgery, foremost, less experienced surgeons may benefit from the AF-guided intraoperative separation of parathyroid from thyroid tissue. There is a growing interest in AF techniques in clinical disciplines, and promising advances have been made during the past decade. However, further research and development are mandatory to overcome the existing limitations and to maximize the clinical benefits

A case study of technology transfer: Cardiology

Research advancements in cardiology instrumentation and techniques are summarized. Emphasis is placed upon the following techniques: (1) development of electrodes which show good skin compatibility and wearer comfort; (2) contourography - a real time display system for showing the results of EKGs; (3) detection of arteriosclerosis by digital computer processing of X-ray photos; (4) automated, noninvasive systems for blood pressure measurement; (5) ultrasonoscope - a noninvasive device for use in diagnosis of aortic, mitral, and tricuspid valve disease; and (6) rechargable cardiac pacemakers. The formation of a biomedical applications team which is an interdisciplinary team to bridge the gap between the developers and users of technology is described

Improved Breath Phase and Continuous Adventitious Sound Detection in Lung and Tracheal Sound Using Mixed Set Training and Domain Adaptation

Previously, we established a lung sound database, HF_Lung_V2 and proposed convolutional bidirectional gated recurrent unit (CNN-BiGRU) models with adequate ability for inhalation, exhalation, continuous adventitious sound (CAS), and discontinuous adventitious sound detection in the lung sound. In this study, we proceeded to build a tracheal sound database, HF_Tracheal_V1, containing 11107 of 15-second tracheal sound recordings, 23087 inhalation labels, 16728 exhalation labels, and 6874 CAS labels. The tracheal sound in HF_Tracheal_V1 and the lung sound in HF_Lung_V2 were either combined or used alone to train the CNN-BiGRU models for respective lung and tracheal sound analysis. Different training strategies were investigated and compared: (1) using full training (training from scratch) to train the lung sound models using lung sound alone and train the tracheal sound models using tracheal sound alone, (2) using a mixed set that contains both the lung and tracheal sound to train the models, and (3) using domain adaptation that finetuned the pre-trained lung sound models with the tracheal sound data and vice versa. Results showed that the models trained only by lung sound performed poorly in the tracheal sound analysis and vice versa. However, the mixed set training and domain adaptation can improve the performance of exhalation and CAS detection in the lung sound, and inhalation, exhalation, and CAS detection in the tracheal sound compared to positive controls (lung models trained only by lung sound and vice versa). Especially, a model derived from the mixed set training prevails in the situation of killing two birds with one stone.Comment: To be submitted, 31 pages, 6 figures, 5 table

Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds

    The study presents a computer-based automated system that employs machine learning to classify pulmonary diseases using lung sound data collected from hospitals. Denoising techniques, such as discrete wavelet transform and variational mode decomposition, are applied to enhance classifier performance. The system combines cepstral features, such as Mel-frequency cepstrum coefficients and gammatone frequency cepstral coefficients, for classification. Four machine learning classifiers, namely the decision tree, k-nearest neighbor, linear discriminant analysis, and random forest, are compared. Evaluation metrics such as accuracy, recall, specificity, and f1 score are employed. This study includes patients affected by chronic obstructive pulmonary disease, asthma, bronchiectasis, and healthy individuals. The results demonstrate that the random forest classifier outperforms the others, achieving an accuracy of 99.72% along with 100% recall, specificity, and f1 scores. The study suggests that the computer-based system serves as a decision-making tool for classifying pulmonary diseases, especially in resource-limited settings

Novel Measurements of Cough and Breathing Abnormalities during Sleep in Cystic Fibrosis

This Doctor of Philosophy thesis describes cystic fibrosis (CF), sleep parameters and novel measurement techniques to determine the effect of lung disease on sleep using non-invasive techniques. Cystic Fibrosis (CF) is characterised by lungs that are normal at birth, but as lung disease progresses with age, adults with CF can develop sleep abnormalities including alteration in sleep architecture and sleep disordered breathing. This thesis seeks to investigate simple non-invasive measures which can detect abnormalities of sleep and breathing in CF adults. The identification of respiratory sounds (normal lung sounds, coughs, crackles, wheezes and snores) will be examined using the non-invasive sleep and breathing measurement device, the Sonomat. The characterisation of these respiratory sounds will be based on spectrographic and audio analysis of the Sonomat. Cross-sectional and longitudinal analysis of adults with CF using polysomnography and the Sonomat will further assess objective sleep and breathing abnormalities. Additional to the examination of objective measurements of sleep, subjective evaluation using CF-specific and sleep-specific questionnaires will assess subjective sleep quality and QoL in adults with CF

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

Computed tomography image analysis for the detection of obstructive lung diseases

Damage to the small airways resulting from direct lung injury or associated with many systemic disorders is not easy to identify. Non-invasive techniques such as chest radiography or conventional tests of lung function often cannot reveal the pathology. On Computed Tomography (CT) images, the signs suggesting the presence of obstructive airways disease are subtle, and inter- and intra-observer variability can be considerable. The goal of this research was to implement a system for the automated analysis of CT data of the lungs. Its function is to help clinicians establish a confident assessment of specific obstructive airways diseases and increase the precision of investigation of structure/function relationships. To help resolve the ambiguities of the CT scans, the main objectives of our system were to provide a functional description of the raster images, extract semi-quantitative measurements of the extent of obstructive airways disease and propose a clinical diagnosis aid using a priori knowledge of CT image features of the diseased lungs. The diagnostic process presented in this thesis involves the extraction and analysis of multiple findings. Several novel low-level computer vision feature extractors and image processing algorithms were developed for extracting the extent of the hypo-attenuated areas, textural characterisation of the lung parenchyma, and morphological description of the bronchi. The fusion of the results of these extractors was achieved with a probabilistic network combining a priori knowledge of lung pathology. Creating a CT lung phantom allowed for the initial validation of the proposed methods. Performance of the techniques was then assessed with clinical trials involving other diagnostic tests and expert chest radiologists. The results of the proposed system for diagnostic decision-support demonstrated the feasibility and importance of information fusion in medical image interpretation.Open acces