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Aujla S, Mohamed A, Tan R, Magtibay K, Tan R, Gao L, Khan N, Umapathy K. Classification of lung pathologies in neonates using dual-tree complex wavelet transform. Biomed Eng Online 2023; 22:115. [PMID: 38049880 PMCID: PMC10696711 DOI: 10.1186/s12938-023-01184-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/27/2023] [Indexed: 12/06/2023] Open
Abstract
INTRODUCTION Undiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which limits the use of LUS, especially in remote areas. An automated point-of-care tool that could screen and capture LUS morphologies associated with neonatal lung pathologies could aid in rapid and accurate diagnosis. METHODS We propose a framework for classifying the six most common neonatal lung pathologies using spatially localized line and texture patterns extracted via 2D dual-tree complex wavelet transform (DTCWT). We acquired 1550 LUS images from 42 neonates with varying numbers of lung pathologies. Furthermore, we balanced our data set to avoid bias towards a pathology class. RESULTS Using DTCWT and clinical features as inputs to a linear discriminant analysis (LDA), our approach achieved a per-image cross-validated classification accuracy of 74.39% for the imbalanced data set. Our classification accuracy improved to 92.78% after balancing our data set. Moreover, our proposed framework achieved a maximum per-subject cross-validated classification accuracy of 64.97% with an imbalanced data set while using a balanced data set improves its classification accuracy up to 81.53%. CONCLUSION Our work could aid in automating the diagnosis of lung pathologies among neonates using LUS. Rapid and accurate diagnosis of lung pathologies could help to decrease neonatal deaths in healthcare institutions that lack well-trained clinicians, especially in developing countries.
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Affiliation(s)
- Sagarjit Aujla
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada.
| | - Adel Mohamed
- Department of Pediatrics, Mount Sinai Hospital, 600 University Ave, Toronto, ON, M5G 1X5, Canada
| | - Ryan Tan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Karl Magtibay
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Randy Tan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Lei Gao
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Naimul Khan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Karthikeyan Umapathy
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
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Singh S, Hoque S, Zekry A, Sowmya A. Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review. J Med Syst 2023; 47:73. [PMID: 37432493 PMCID: PMC10335966 DOI: 10.1007/s10916-023-01968-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 07/02/2023] [Indexed: 07/12/2023]
Abstract
Medical image analysis plays a pivotal role in the evaluation of diseases, including screening, surveillance, diagnosis, and prognosis. Liver is one of the major organs responsible for key functions of metabolism, protein and hormone synthesis, detoxification, and waste excretion. Patients with advanced liver disease and Hepatocellular Carcinoma (HCC) are often asymptomatic in the early stages; however delays in diagnosis and treatment can lead to increased rates of decompensated liver diseases, late-stage HCC, morbidity and mortality. Ultrasound (US) is commonly used imaging modality for diagnosis of chronic liver diseases that includes fibrosis, cirrhosis and portal hypertension. In this paper, we first provide an overview of various diagnostic methods for stages of liver diseases and discuss the role of Computer-Aided Diagnosis (CAD) systems in diagnosing liver diseases. Second, we review the utility of machine learning and deep learning approaches as diagnostic tools. Finally, we present the limitations of existing studies and outline future directions to further improve diagnostic accuracy, as well as reduce cost and subjectivity, while also improving workflow for the clinicians.
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Affiliation(s)
- Sonit Singh
- School of CSE, UNSW Sydney, High St, Kensington, 2052, NSW, Australia.
| | - Shakira Hoque
- Gastroenterology and Hepatology Department, St George Hospital, Hogben St, Kogarah, 2217, NSW, Australia
| | - Amany Zekry
- St George and Sutherland Clinical Campus, School of Clinical Medicine, UNSW, High St, Kensington, 2052, NSW, Australia
- Gastroenterology and Hepatology Department, St George Hospital, Hogben St, Kogarah, 2217, NSW, Australia
| | - Arcot Sowmya
- School of CSE, UNSW Sydney, High St, Kensington, 2052, NSW, Australia
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Chen Q, Shi T, Du D, Wang B, Zhao S, Gao Y, Wang S, Zhang Z. Non-destructive diagnostic testing of cardiac myxoma by serum confocal Raman microspectroscopy combined with multivariate analysis. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:2578-2587. [PMID: 37114381 DOI: 10.1039/d3ay00180f] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The symptoms of cardiac myxoma (CM) mainly occur when the tumor is growing, and the diagnosis is determined by clinical presentation. Unfortunately, there is no evidence that specific blood tests are useful in CM diagnosis. Raman spectroscopy (RS) has emerged as a promising auxiliary diagnostic tool because of its ability to simultaneously detect multiple molecular features without labelling. The objective of this study was to identify spectral markers for CM, one of the most common benign cardiac tumors with insidious onset and rapid progression. In this study, a preliminary analysis was conducted based on serum Raman spectra to obtain the spectral differences between CM patients (CM group) and healthy control subjects (normal group). Principal component analysis-linear discriminant analysis (PCA-LDA) was constructed to highlight the differences in the distribution of biochemical components among the groups according to the obtained spectral information. Principal component analysis was combined with a support vector machine model (PCA-SVM) based on three different kernel functions (linear, polynomial, and Gaussian radial basis function (RBF)) to resolve spectral variations between all study groups. The results showed that CM patients had lower serum levels of phenylalanine and carotenoid than those in the normal group, and increased levels of fatty acids. The resulting Raman data was used in a multivariate analysis to determine the Raman range that could be used for CM diagnosis. Also, the chemical interpretation of the spectral results obtained is further presented in the discussion section based on the multivariate curve resolution-alternating least squares (MCR-ALS) method. These results suggest that RS can be used as an adjunct and promising tool for CM diagnosis, and that vibrations in the fingerprint region can be used as spectral markers for the disease under study.
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Affiliation(s)
- Qiang Chen
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Tao Shi
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Dan Du
- Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Bo Wang
- Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Sha Zhao
- Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Yang Gao
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Shuang Wang
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, China
| | - Zhanqin Zhang
- Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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Wang X, Sheng L. Correlations between B-mode ultrasound image texture features and tissue temperatures in hyperthermia. PLoS One 2022; 17:e0266446. [PMID: 36201496 PMCID: PMC9536603 DOI: 10.1371/journal.pone.0266446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 03/22/2022] [Indexed: 11/19/2022] Open
Abstract
PURPOSE The noninvasive monitoring of mild hyperthermia or thermal ablation is important to guarantee therapeutic safety and efficacy. The potential of ultrasound B-mode image texture features in monitoring temperature or coagulation zones studied in this article. MATERIALS AND METHODS The experiments carried out on eighteen in vitro porcine liver samples heated from 20°C to 60°C in the water bath. The ultrasound radiofrequency signal at different temperature collected to reconstruct B-mode ultrasound images. The texture features based on gray level histogram (GLH), gray level co-occurrence matrix (GLCM), and gray level-gradient co-occurrence matrix (GGCM) extracted, respectively. Accordingly, we analyze the correlations between these texture features and temperature based on the experiment results. RESULTS The results showed that five texture feature parameters closely related to temperature, including mean gray scale of GLH, homogeneity of GLCM, hybrid entropy, inverse difference moment, and correlation of GGCM. Some of these feature parameters have correlation coefficients larger than 0.9 within the temperature range of 20°C to 60°C. CONCLUSIONS The above-mentioned five feature parameters expected to apply for noninvasive monitoring of MH or TA.
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Affiliation(s)
- Xuelin Wang
- School of Engineering Medicine, Beihang University, Beijing, China
| | - Lei Sheng
- Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing, China
- * E-mail:
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A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis. Sci Rep 2021; 11:15546. [PMID: 34330946 PMCID: PMC8324876 DOI: 10.1038/s41598-021-94781-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 07/09/2021] [Indexed: 02/07/2023] Open
Abstract
Colorectal cancer (CRC) constitutes the third most commonly diagnosed cancer in males and the second in females. Precise histopathological classification of CRC tissue pathology is the cornerstone not only for diagnosis but also for patients' management decision making. An automated system able to accurately classify different CRC tissue regions may increase diagnostic precision and alleviate clinical workload. However, tissue classification is a challenging task due to the variability in morphological and textural characteristics present in histopathology images. In this study, an artificial neural network was trained to classify between eight classes of CRC tissue image patches derived from a public dataset with 5000 CRC histopathology image tiles. A total of 532 multi-level pathomics features examined at different scales were extracted by visual descriptors such as local binary patterns, wavelet transforms and Gabor filters. An exhaustive evaluation involving a variety of wavelet families and parameters was performed in order to shed light on the impact of scale on pathomics based CRC tissue differentiation. Our model achieved a performance accuracy of 95.3% with tenfold cross validation demonstrating superior performance compared to 87.4% reported in recent studies. Furthermore, we experimentally showed that the first and the second levels of the wavelet approximations can be used without compromising classification performance.
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Gutiérrez-Martínez J, Pineda C, Sandoval H, Bernal-González A. Computer-aided diagnosis in rheumatic diseases using ultrasound: an overview. Clin Rheumatol 2019; 39:993-1005. [DOI: 10.1007/s10067-019-04791-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 08/07/2019] [Accepted: 09/21/2019] [Indexed: 12/12/2022]
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