1
|
Rinaldi L, Perrotta F. Application and internal validation of lung ultrasound score in COVID-19 setting: The ECOVITA observational study. Authors' reply. Pulmonology 2025; 31:2416781. [PMID: 39003186 DOI: 10.1016/j.pulmoe.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Revised: 06/08/2024] [Accepted: 06/10/2024] [Indexed: 07/15/2024] Open
Affiliation(s)
- L Rinaldi
- Department of Medicine and Health Sciences "V. Tiberio", Università degli Studi del Molise, Campobasso, Italy
| | - F Perrotta
- Department of Translational Medical Sciences, University of Campania L. Vanvitelli, "Monaldi" Hospital, Naples, Italy
| |
Collapse
|
2
|
Zhang C, Mei M, Mei Z, Wu B, Chen S, Lu M, Lu C. On efficient expanding training datasets of breast tumor ultrasound segmentation model. Comput Biol Med 2024; 183:109274. [PMID: 39471661 DOI: 10.1016/j.compbiomed.2024.109274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 09/25/2024] [Accepted: 10/10/2024] [Indexed: 11/01/2024]
Abstract
Automatic segmentation of breast tumor ultrasound images can provide doctors with objective and efficient references for lesions and regions of interest. Both dataset optimization and model structure optimization are crucial for achieving optimal image segmentation performance, and it can be challenging to satisfy the clinical needs solely through model structure enhancements in the context of insufficient breast tumor ultrasound datasets for model training. While significant research has focused on enhancing the architecture of deep learning models to improve tumor segmentation performance, there is a relative paucity of work dedicated to dataset augmentation. Current data augmentation techniques, such as rotation and transformation, often yield insufficient improvements in model accuracy. The deep learning methods used for generating synthetic images, such as GANs is primarily applied to produce visually natural-looking images. Nevertheless, the accuracy of the labels for these generated images still requires manual verification, and the images exhibit a lack of diversity. Therefore, they are not suitable for the training datasets augmentation of image segmentation models. This study introduces a novel dataset augmentation approach that generates synthetic images by embedding tumor regions into normal images. We explore two synthetic methods: one using identical backgrounds and another with varying backgrounds. Through experimental validation, we demonstrate the efficiency of the synthetic datasets in enhancing the performance of image segmentation models. Notably, the synthetic method utilizing different backgrounds exhibits superior improvement compared to the identical background approach. Our findings contribute to medical image analysis, particularly in tumor segmentation, by providing a practical and effective dataset augmentation strategy that can significantly improve the accuracy and reliability of segmentation models.
Collapse
Affiliation(s)
- Caicai Zhang
- School of Modern Information Technology, Zhejiang Polytechnic University of Mechanical and Electrical Engineering, 528 Binwen Road, Binjiang District, Hangzhou, 310053, Zhejiang, China.
| | - Mei Mei
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Road, Shangcheng District, Hangzhou 310009, Zhejiang, China.
| | - Zhuolin Mei
- School of Computer and Big Data Science, Jiujiang University, 551 Qianjin East Road, Jiujiang 332005, Jiangxi, China.
| | - Bin Wu
- School of Computer and Big Data Science, Jiujiang University, 551 Qianjin East Road, Jiujiang 332005, Jiangxi, China.
| | - Shasha Chen
- School of Modern Information Technology, Zhejiang Polytechnic University of Mechanical and Electrical Engineering, 528 Binwen Road, Binjiang District, Hangzhou, 310053, Zhejiang, China.
| | - Minfeng Lu
- School of Modern Information Technology, Zhejiang Polytechnic University of Mechanical and Electrical Engineering, 528 Binwen Road, Binjiang District, Hangzhou, 310053, Zhejiang, China.
| | - Chenglang Lu
- School of Modern Information Technology, Zhejiang Polytechnic University of Mechanical and Electrical Engineering, 528 Binwen Road, Binjiang District, Hangzhou, 310053, Zhejiang, China.
| |
Collapse
|
3
|
Duggan NM, Jin M, Duran Mendicuti MA, Hallisey S, Bernier D, Selame LA, Asgari-Targhi A, Fischetti CE, Lucassen R, Samir AE, Duhaime E, Kapur T, Goldsmith AJ. Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis. J Med Internet Res 2024; 26:e51397. [PMID: 38963923 PMCID: PMC11258523 DOI: 10.2196/51397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/04/2023] [Accepted: 04/10/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality. OBJECTIVE This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data. METHODS In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips. RESULTS Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance. CONCLUSIONS Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.
Collapse
Affiliation(s)
- Nicole M Duggan
- Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Mike Jin
- Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Centaur Labs, Boston, MA, United States
| | | | - Stephen Hallisey
- Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Denie Bernier
- Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Lauren A Selame
- Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Ameneh Asgari-Targhi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Chanel E Fischetti
- Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Ruben Lucassen
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Anthony E Samir
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | | | - Tina Kapur
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Andrew J Goldsmith
- Department of Emergency Medicine, Lahey Hospital, University of Massachusetts Medical School, Burlington, MA, United States
| |
Collapse
|
4
|
Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, Dar SA. The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms 2024; 12:1051. [PMID: 38930432 PMCID: PMC11205376 DOI: 10.3390/microorganisms12061051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.
Collapse
Affiliation(s)
- Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Naseem Akhter
- Department of Biology, Arizona State University, Lake Havasu City, AZ 86403, USA;
| | - Fatima Jameela
- Modern American Dental Clinic, West Warren Avenue, Dearborn, MI 48126, USA;
| | - Rnda I. Ashgar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Arshad Jawed
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Mohammed Ahmed Hassani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Sajad Ahmad Dar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| |
Collapse
|
5
|
Howell L, Ingram N, Lapham R, Morrell A, McLaughlan JR. Deep learning for real-time multi-class segmentation of artefacts in lung ultrasound. ULTRASONICS 2024; 140:107251. [PMID: 38520819 DOI: 10.1016/j.ultras.2024.107251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 12/20/2023] [Accepted: 01/17/2024] [Indexed: 03/25/2024]
Abstract
Lung ultrasound (LUS) has emerged as a safe and cost-effective modality for assessing lung health, particularly during the COVID-19 pandemic. However, interpreting LUS images remains challenging due to its reliance on artefacts, leading to operator variability and limiting its practical uptake. To address this, we propose a deep learning pipeline for multi-class segmentation of objects (ribs, pleural line) and artefacts (A-lines, B-lines, B-line confluence) in ultrasound images of a lung training phantom. Lightweight models achieved a mean Dice Similarity Coefficient (DSC) of 0.74, requiring fewer than 500 training images. Applying this method in real-time, at up to 33.4 frames per second in inference, allows enhanced visualisation of these features in LUS images. This could be useful in providing LUS training and helping to address the skill gap. Moreover, the segmentation masks obtained from this model enable the development of explainable measures of disease severity, which have the potential to assist in the triage and management of patients. We suggest one such semi-quantitative measure called the B-line Artefact Score, which is related to the percentage of an intercostal space occupied by B-lines and in turn may be associated with the severity of a number of lung conditions. Moreover, we show how transfer learning could be used to train models for small datasets of clinical LUS images, identifying pathologies such as simple pleural effusions and lung consolidation with DSC values of 0.48 and 0.32 respectively. Finally, we demonstrate how such DL models could be translated into clinical practice, implementing the phantom model alongside a portable point-of-care ultrasound system, facilitating bedside assessment and improving the accessibility of LUS.
Collapse
Affiliation(s)
- Lewis Howell
- School of Computing, University of Leeds, Leeds, LS2 9JT, UK; School of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Nicola Ingram
- Leeds Institute of Medical Research, University of Leeds, St James' University Hospital, Leeds, LS9 7TF, UK
| | - Roger Lapham
- Radiology Department, Leeds Teaching Hospital Trust, Leeds General Infirmary, Leeds, LS1 3EX, UK
| | - Adam Morrell
- Radiology Department, Leeds Teaching Hospital Trust, Leeds General Infirmary, Leeds, LS1 3EX, UK
| | - James R McLaughlan
- School of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, UK; Leeds Institute of Medical Research, University of Leeds, St James' University Hospital, Leeds, LS9 7TF, UK.
| |
Collapse
|
6
|
Wang R, Liu X, Tan G. Coupling speckle noise suppression with image classification for deep-learning-aided ultrasound diagnosis. Phys Med Biol 2024; 69:065001. [PMID: 38359452 DOI: 10.1088/1361-6560/ad29bb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 02/15/2024] [Indexed: 02/17/2024]
Abstract
Objective. During deep-learning-aided (DL-aided) ultrasound (US) diagnosis, US image classification is a foundational task. Due to the existence of serious speckle noise in US images, the performance of DL models may be degraded. Pre-denoising US images before their use in DL models is usually a logical choice. However, our investigation suggests that pre-speckle-denoising is not consistently advantageous. Furthermore, due to the decoupling of speckle denoising from the subsequent DL classification, investing intensive time in parameter tuning is inevitable to attain the optimal denoising parameters for various datasets and DL models. Pre-denoising will also add extra complexity to the classification task and make it no longer end-to-end.Approach. In this work, we propose a multi-scale high-frequency-based feature augmentation (MSHFFA) module that couples feature augmentation and speckle noise suppression with specific DL models, preserving an end-to-end fashion. In MSHFFA, the input US image is first decomposed to multi-scale low-frequency and high-frequency components (LFC and HFC) with discrete wavelet transform. Then, multi-scale augmentation maps are obtained by computing the correlation between LFC and HFC. Last, the original DL model features are augmented with multi-scale augmentation maps.Main results. On two public US datasets, all six renowned DL models exhibited enhanced F1-scores compared with their original versions (by 1.31%-8.17% on the POCUS dataset and 0.46%-3.89% on the BLU dataset) after using the MSHFFA module, with only approximately 1% increase in model parameter count.Significance. The proposed MSHFFA has broad applicability and commendable efficiency and thus can be used to enhance the performance of DL-aided US diagnosis. The codes are available athttps://github.com/ResonWang/MSHFFA.
Collapse
Affiliation(s)
- Ruixin Wang
- College of Computer Science and Software Engineering, Hohai University, Nanjing 210098, People's Republic of China
| | - Xiaohui Liu
- The First People's Hospital of Kunshan, Affiliated Kunshan Hospital of Jiangsu University, Kunshan 215300, People's Republic of China
| | - Guoping Tan
- College of Computer Science and Software Engineering, Hohai University, Nanjing 210098, People's Republic of China
| |
Collapse
|
7
|
Baddal B, Taner F, Uzun Ozsahin D. Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review. Diagnostics (Basel) 2024; 14:484. [PMID: 38472956 DOI: 10.3390/diagnostics14050484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/23/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.
Collapse
Affiliation(s)
- Buket Baddal
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Ferdiye Taner
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| |
Collapse
|
8
|
Yerlikaya S, Broger T, Isaacs C, Bell D, Holtgrewe L, Gupta-Wright A, Nahid P, Cattamanchi A, Denkinger CM. Blazing the trail for innovative tuberculosis diagnostics. Infection 2024; 52:29-42. [PMID: 38032537 PMCID: PMC10811035 DOI: 10.1007/s15010-023-02135-3] [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: 08/21/2023] [Accepted: 11/01/2023] [Indexed: 12/01/2023]
Abstract
The COVID-19 pandemic brought diagnostics into the spotlight in an unprecedented way not only for case management but also for population health, surveillance, and monitoring. The industry saw notable levels of investment and accelerated research which sparked a wave of innovation. Simple non-invasive sampling methods such as nasal swabs have become widely used in settings ranging from tertiary hospitals to the community. Self-testing has also been adopted as standard practice using not only conventional lateral flow tests but novel and affordable point-of-care molecular diagnostics. The use of new technologies, including artificial intelligence-based diagnostics, have rapidly expanded in the clinical setting. The capacity for next-generation sequencing and acceptance of digital health has significantly increased. However, 4 years after the pandemic started, the market for SARS-CoV-2 tests is saturated, and developers may benefit from leveraging their innovations for other diseases; tuberculosis (TB) is a worthwhile portfolio expansion for diagnostics developers given the extremely high disease burden, supportive environment from not-for-profit initiatives and governments, and the urgent need to overcome the long-standing dearth of innovation in the TB diagnostics field. In exchange, the current challenges in TB detection may be resolved by adopting enhanced swab-based molecular methods, instrument-based, higher sensitivity antigen detection technologies, and/or artificial intelligence-based digital health technologies developed for COVID-19. The aim of this article is to review how such innovative approaches for COVID-19 diagnosis can be applied to TB to have a comparable impact.
Collapse
Affiliation(s)
- Seda Yerlikaya
- Division of Infectious Diseases and Tropical Medicine, Heidelberg University Hospital and Faculty of Medicine, Heidelberg University, Heidelberg, Germany.
| | - Tobias Broger
- Division of Infectious Diseases and Tropical Medicine, Heidelberg University Hospital and Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | | | - David Bell
- Independent Consultant, Lake Jackson, TX, USA
| | - Lydia Holtgrewe
- Division of Infectious Diseases and Tropical Medicine, Heidelberg University Hospital and Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Ankur Gupta-Wright
- Division of Infectious Diseases and Tropical Medicine, Heidelberg University Hospital and Faculty of Medicine, Heidelberg University, Heidelberg, Germany
- Institute for Global Health, University College London, London, UK
| | - Payam Nahid
- UCSF Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA
| | - Adithya Cattamanchi
- UCSF Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA
- Division of Pulmonary Diseases and Critical Care Medicine, University of California Irvine, Irvine, CA, USA
| | - Claudia M Denkinger
- Division of Infectious Diseases and Tropical Medicine, Heidelberg University Hospital and Faculty of Medicine, Heidelberg University, Heidelberg, Germany
- German Centre for Infection Research, Partner Site Heidelberg University Hospital, Heidelberg, Germany
| |
Collapse
|
9
|
Marozzi MS, Cicco S, Mancini F, Corvasce F, Lombardi FA, Desantis V, Loponte L, Giliberti T, Morelli CM, Longo S, Lauletta G, Solimando AG, Ria R, Vacca A. A Novel Automatic Algorithm to Support Lung Ultrasound Non-Expert Physicians in Interstitial Pneumonia Evaluation: A Single-Center Study. Diagnostics (Basel) 2024; 14:155. [PMID: 38248032 PMCID: PMC10814651 DOI: 10.3390/diagnostics14020155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/06/2024] [Accepted: 01/07/2024] [Indexed: 01/23/2024] Open
Abstract
INTRODUCTION Lung ultrasound (LUS) is widely used in clinical practice for identifying interstitial lung diseases (ILDs) and assessing their progression. Although high-resolution computed tomography (HRCT) remains the gold standard for evaluating the severity of ILDs, LUS can be performed as a screening method or as a follow-up tool post-HRCT. Minimum training is needed to better identify typical lesions, and the integration of innovative artificial intelligence (AI) automatic algorithms may enhance diagnostic efficiency. AIM This study aims to assess the effectiveness of a novel AI algorithm in automatic ILD recognition and scoring in comparison to an expert LUS sonographer. The "SensUS Lung" device, equipped with an automatic algorithm, was employed for the automatic recognition of the typical ILD patterns and to calculate an index grading of the interstitial involvement. METHODS We selected 33 Caucasian patients in follow-up for ILDs exhibiting typical HRCT patterns (honeycombing, ground glass, fibrosis). An expert physician evaluated all patients with LUS on twelve segments (six per side). Next, blinded to the previous evaluation, an untrained operator, a non-expert in LUS, performed the exam with the SensUS device equipped with the automatic algorithm ("SensUS Lung") using the same protocol. Pulmonary functional tests (PFT) and DLCO were conducted for all patients, categorizing them as having reduced or preserved DLCO. The SensUS device indicated different grades of interstitial involvement named Lung Staging that were scored from 0 (absent) to 4 (peak), which was compared to the Lung Ultrasound Score (LUS score) by dividing it by the number of segments evaluated. Statistical analyses were done with Wilcoxon tests for paired values or Mann-Whitney for unpaired samples, and correlations were performed using Spearman analysis; p < 0.05 was considered significant. RESULTS Lung Staging was non-inferior to LUS score in identifying the risk of ILDs (median SensUS 1 [0-2] vs. LUS 0.67 [0.25-1.54]; p = 0.84). Furthermore, the grade of interstitial pulmonary involvement detected with the SensUS device is directly related to the LUS score (r = 0.607, p = 0.002). Lung Staging values were inversely correlated with forced expiratory volume at first second (FEV1%, r = -0.40, p = 0.027), forced vital capacity (FVC%, r = -0.39, p = 0.03) and forced expiratory flow (FEF) at 25th percentile (FEF25%, r = -0.39, p = 0.02) while results directly correlated with FEF25-75% (r = 0.45, p = 0.04) and FEF75% (r = 0.43, p = 0.01). Finally, in patients with reduced DLCO, the Lung Staging was significantly higher, overlapping the LUS (reduced median 1 [1-2] vs. preserved 0 [0-1], p = 0.001), and overlapping the LUS (reduced median 18 [4-20] vs. preserved 5.5 [2-9], p = 0.035). CONCLUSIONS Our data suggest that the considered AI automatic algorithm may assist non-expert physicians in LUS, resulting in non-inferior-to-expert LUS despite a tendency to overestimate ILD lesions. Therefore, the AI algorithm has the potential to support physicians, particularly non-expert LUS sonographers, in daily clinical practice to monitor patients with ILDs. The adopted device is user-friendly, offering a fully automatic real-time analysis. However, it needs proper training in basic skills.
Collapse
Affiliation(s)
- Marialuisa Sveva Marozzi
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Sebastiano Cicco
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Francesca Mancini
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Francesco Corvasce
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | | | - Vanessa Desantis
- Pharmacology Section, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Luciana Loponte
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Tiziana Giliberti
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Claudia Maria Morelli
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Stefania Longo
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Gianfranco Lauletta
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Antonio G. Solimando
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Roberto Ria
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Angelo Vacca
- Unit of Internal Medicine “G. Baccelli”, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Interdepartmental Centre for Research in Telemedicine (CITEL), Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| |
Collapse
|
10
|
Buongiorno R, Del Corso G, Germanese D, Colligiani L, Python L, Romei C, Colantonio S. Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models. J Imaging 2023; 9:283. [PMID: 38132701 PMCID: PMC10744014 DOI: 10.3390/jimaging9120283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/09/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
Imaging plays a key role in the clinical management of Coronavirus disease 2019 (COVID-19) as the imaging findings reflect the pathological process in the lungs. The visual analysis of High-Resolution Computed Tomography of the chest allows for the differentiation of parenchymal abnormalities of COVID-19, which are crucial to be detected and quantified in order to obtain an accurate disease stratification and prognosis. However, visual assessment and quantification represent a time-consuming task for radiologists. In this regard, tools for semi-automatic segmentation, such as those based on Convolutional Neural Networks, can facilitate the detection of pathological lesions by delineating their contour. In this work, we compared four state-of-the-art Convolutional Neural Networks based on the encoder-decoder paradigm for the binary segmentation of COVID-19 infections after training and testing them on 90 HRCT volumetric scans of patients diagnosed with COVID-19 collected from the database of the Pisa University Hospital. More precisely, we started from a basic model, the well-known UNet, then we added an attention mechanism to obtain an Attention-UNet, and finally we employed a recurrence paradigm to create a Recurrent-Residual UNet (R2-UNet). In the latter case, we also added attention gates to the decoding path of an R2-UNet, thus designing an R2-Attention UNet so as to make the feature representation and accumulation more effective. We compared them to gain understanding of both the cognitive mechanism that can lead a neural model to the best performance for this task and the good compromise between the amount of data, time, and computational resources required. We set up a five-fold cross-validation and assessed the strengths and limitations of these models by evaluating the performances in terms of Dice score, Precision, and Recall defined both on 2D images and on the entire 3D volume. From the results of the analysis, it can be concluded that Attention-UNet outperforms the other models by achieving the best performance of 81.93%, in terms of 2D Dice score, on the test set. Additionally, we conducted statistical analysis to assess the performance differences among the models. Our findings suggest that integrating the recurrence mechanism within the UNet architecture leads to a decline in the model's effectiveness for our particular application.
Collapse
Affiliation(s)
- Rossana Buongiorno
- Institute of Information Science and Technologies, National Research Council of Italy (ISTI-CNR), 56124 Pisa, PI, Italy; (G.D.C.); (S.C.)
| | - Giulio Del Corso
- Institute of Information Science and Technologies, National Research Council of Italy (ISTI-CNR), 56124 Pisa, PI, Italy; (G.D.C.); (S.C.)
| | - Danila Germanese
- Institute of Information Science and Technologies, National Research Council of Italy (ISTI-CNR), 56124 Pisa, PI, Italy; (G.D.C.); (S.C.)
| | - Leonardo Colligiani
- Department of Translational Research, Academic Radiology, University of Pisa, 56124 Pisa, PI, Italy;
| | - Lorenzo Python
- 2nd Radiology Unit, Pisa University Hospital, 56124 Pisa, PI, Italy; (L.P.)
| | - Chiara Romei
- 2nd Radiology Unit, Pisa University Hospital, 56124 Pisa, PI, Italy; (L.P.)
| | - Sara Colantonio
- Institute of Information Science and Technologies, National Research Council of Italy (ISTI-CNR), 56124 Pisa, PI, Italy; (G.D.C.); (S.C.)
| |
Collapse
|
11
|
Sultan LR, Haertter A, Al-Hasani M, Demiris G, Cary TW, Tung-Chen Y, Sehgal CM. Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound. AI 2023; 4:875-887. [PMID: 37929255 PMCID: PMC10623579 DOI: 10.3390/ai4040044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2023] Open
Abstract
With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures. In this paper, we propose the use of teleguided POCUS supported by AI technologies for the remote monitoring of COVID-19 patients by non-experienced personnel including self-monitoring by the patients themselves. Our hypothesis is that AI technologies can facilitate the remote monitoring of COVID-19 patients through the utilization of POCUS devices, even when operated by individuals without formal medical training. In pursuit of this goal, we performed a pilot analysis to evaluate the performance of users with different clinical backgrounds using a computer-based system for COVID-19 detection using lung ultrasound. The purpose of the analysis was to emphasize the potential of the proposed AI technology for improving diagnostic performance, especially for users with less experience.
Collapse
Affiliation(s)
- Laith R. Sultan
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Allison Haertter
- Radiation Oncology Department, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Maryam Al-Hasani
- Ultrasound Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19103, USA
| | - George Demiris
- Informatics Division of the Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore W. Cary
- Ultrasound Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19103, USA
| | - Yale Tung-Chen
- Emergency Medicine Department, La Madrida Hospital, 28006 Madrid, Spain
| | - Chandra M. Sehgal
- Ultrasound Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19103, USA
| |
Collapse
|
12
|
Scarlata S, Okoye C, Zotti S, Lauretani F, Nouvenne A, Cerundolo N, Bruni AA, Torrini M, Finazzi A, Mazzarone T, Lunian M, Zucchini I, Maccioni L, Guarino D, Fabbri Della Faggiola S, Capacci M, Bianco MG, Guarona G, Bellelli G, Monzani F, Virdis A, Antonelli Incalzi R, Ungar A, Ticinesi A. Advancing healthcare through thoracic ultrasound research in older patients. Aging Clin Exp Res 2023; 35:2887-2901. [PMID: 37950845 PMCID: PMC10721707 DOI: 10.1007/s40520-023-02590-w] [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: 09/27/2023] [Accepted: 10/08/2023] [Indexed: 11/13/2023]
Abstract
This paper reports the proceedings of a meeting convened by the Research Group on Thoracic Ultrasound in Older People of the Italian Society of Gerontology and Geriatrics, to discuss the current state-of-the-art of clinical research in the field of geriatric thoracic ultrasound and identify unmet research needs and potential areas of development. In the last decade, point-of-care thoracic ultrasound has entered clinical practice for diagnosis and management of several respiratory illnesses, such as bacterial and viral pneumonia, pleural effusion, acute heart failure, and pneumothorax, especially in the emergency-urgency setting. Very few studies, however, have been specifically focused on older patients with frailty and multi-morbidity, who frequently exhibit complex clinical pictures needing multidimensional evaluation. At the present state of knowledge, there is still uncertainty on the best requirements of ultrasound equipment, methodology of examination, and reporting needed to optimize the advantages of thoracic ultrasound implementation in the care of geriatric patients. Other issues regard differential diagnosis between bacterial and aspiration pneumonia, objective grading of interstitial syndrome severity, quantification and monitoring of pleural effusions and solid pleural lesions, significance of ultrasonographic assessment of post-COVID-19 sequelae, and prognostic value of assessment of diaphragmatic thickness and motility. Finally, application of remote ultrasound diagnostics in the community and nursing home setting is still poorly investigated by the current literature. Overall, the presence of several open questions on geriatric applications of thoracic ultrasound represents a strong call to implement clinical research in this field.
Collapse
Affiliation(s)
- Simone Scarlata
- Operative Research Unit of Internal Medicine, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128, Rome, Italy
- Department of Medicine and Surgery, Research Unit of Geriatrics, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | - Chukwuma Okoye
- School of Medicine and Surgery, University of Milano-Bicocca, Via Giovanni Battista Pergolesi 33, 20900, Monza, Italy.
- Department of Neurobiology, Care Sciences and Society, Department of Geriatrics Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
| | - Sonia Zotti
- Department of Medicine and Surgery, Research Unit of Geriatrics, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | - Fulvio Lauretani
- Department of Medicine and Surgery, University of Parma, Via Antonio Gramsci 14, 43126, Parma, Italy
- Geriatric-Rehabilitation Department, Azienda Ospedaliero-Universitaria di Parma, Via Antonio Gramsci 14, 43126, Parma, Italy
| | - Antonio Nouvenne
- Department of Medicine and Surgery, University of Parma, Via Antonio Gramsci 14, 43126, Parma, Italy
- Geriatric-Rehabilitation Department, Azienda Ospedaliero-Universitaria di Parma, Via Antonio Gramsci 14, 43126, Parma, Italy
| | - Nicoletta Cerundolo
- Department of Medicine and Surgery, University of Parma, Via Antonio Gramsci 14, 43126, Parma, Italy
- Geriatric-Rehabilitation Department, Azienda Ospedaliero-Universitaria di Parma, Via Antonio Gramsci 14, 43126, Parma, Italy
| | - Adriana Antonella Bruni
- Acute Geriatric Unit, Fondazione IRCCS San Gerardo de Tintori, Via Giovanni Battista Pergolesi 33, 20900, Monza, Italy
| | - Monica Torrini
- Geriatrics and Intensive Care Unit, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Alberto Finazzi
- School of Medicine and Surgery, University of Milano-Bicocca, Via Giovanni Battista Pergolesi 33, 20900, Monza, Italy
| | - Tessa Mazzarone
- Department of Clinical and Experimental Medicine, University of Pisa, Via Paradisa, 2, 56124, Pisa, Italy
| | - Marco Lunian
- Department of Medicine and Surgery, University of Parma, Via Antonio Gramsci 14, 43126, Parma, Italy
| | - Irene Zucchini
- Geriatric-Rehabilitation Department, Azienda Ospedaliero-Universitaria di Parma, Via Antonio Gramsci 14, 43126, Parma, Italy
| | - Lorenzo Maccioni
- Department of Clinical and Experimental Medicine, University of Pisa, Via Paradisa, 2, 56124, Pisa, Italy
| | - Daniela Guarino
- Department of Clinical and Experimental Medicine, University of Pisa, Via Paradisa, 2, 56124, Pisa, Italy
| | | | - Marco Capacci
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Maria Giovanna Bianco
- Department of Clinical and Experimental Medicine, University of Pisa, Via Paradisa, 2, 56124, Pisa, Italy
| | - Guglielmo Guarona
- Department of Medicine and Surgery, University of Parma, Via Antonio Gramsci 14, 43126, Parma, Italy
| | - Giuseppe Bellelli
- School of Medicine and Surgery, University of Milano-Bicocca, Via Giovanni Battista Pergolesi 33, 20900, Monza, Italy
- Acute Geriatric Unit, Fondazione IRCCS San Gerardo de Tintori, Via Giovanni Battista Pergolesi 33, 20900, Monza, Italy
| | - Fabio Monzani
- Casa di Cura Venerabile Confraternita di Misericordia Navacchio, 56023, Pisa, Italy
| | - Agostino Virdis
- Department of Clinical and Experimental Medicine, University of Pisa, Via Paradisa, 2, 56124, Pisa, Italy
| | - Raffaele Antonelli Incalzi
- Operative Research Unit of Internal Medicine, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128, Rome, Italy
- Department of Medicine and Surgery, Research Unit of Geriatrics, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo 200, 00128, Rome, Italy
| | - Andrea Ungar
- Geriatrics and Intensive Care Unit, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Andrea Ticinesi
- Department of Medicine and Surgery, University of Parma, Via Antonio Gramsci 14, 43126, Parma, Italy
- Geriatric-Rehabilitation Department, Azienda Ospedaliero-Universitaria di Parma, Via Antonio Gramsci 14, 43126, Parma, Italy
| |
Collapse
|
13
|
Malík M, Dzian A, Števík M, Vetešková Š, Al Hakim A, Hliboký M, Magyar J, Kolárik M, Bundzel M, Babič F. Lung Ultrasound Reduces Chest X-rays in Postoperative Care after Thoracic Surgery: Is There a Role for Artificial Intelligence?-Systematic Review. Diagnostics (Basel) 2023; 13:2995. [PMID: 37761362 PMCID: PMC10527627 DOI: 10.3390/diagnostics13182995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/16/2023] [Accepted: 08/26/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Chest X-ray (CXR) remains the standard imaging modality in postoperative care after non-cardiac thoracic surgery. Lung ultrasound (LUS) showed promising results in CXR reduction. The aim of this review was to identify areas where the evaluation of LUS videos by artificial intelligence could improve the implementation of LUS in thoracic surgery. METHODS A literature review of the replacement of the CXR by LUS after thoracic surgery and the evaluation of LUS videos by artificial intelligence after thoracic surgery was conducted in Medline. RESULTS Here, eight out of 10 reviewed studies evaluating LUS in CXR reduction showed that LUS can reduce CXR without a negative impact on patient outcome after thoracic surgery. No studies on the evaluation of LUS signs by artificial intelligence after thoracic surgery were found. CONCLUSION LUS can reduce CXR after thoracic surgery. We presume that artificial intelligence could help increase the LUS accuracy, objectify the LUS findings, shorten the learning curve, and decrease the number of inconclusive results. To confirm this assumption, clinical trials are necessary. This research is funded by the Slovak Research and Development Agency, grant number APVV 20-0232.
Collapse
Affiliation(s)
- Marek Malík
- Department of Thoracic Surgery, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Kollárova 4248/2, 036 59 Martin, Slovakia
| | - Anton Dzian
- Department of Thoracic Surgery, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Kollárova 4248/2, 036 59 Martin, Slovakia
| | - Martin Števík
- Radiology Department, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Kollárova 4248/2, 036 59 Martin, Slovakia
| | - Štefánia Vetešková
- Radiology Department, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Kollárova 4248/2, 036 59 Martin, Slovakia
| | - Abdulla Al Hakim
- Department of Thoracic Surgery, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Kollárova 4248/2, 036 59 Martin, Slovakia
| | - Maroš Hliboký
- Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia
| | - Ján Magyar
- Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia
| | - Michal Kolárik
- Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia
| | - Marek Bundzel
- Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia
| | - František Babič
- Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia
| |
Collapse
|
14
|
Sagreiya H, Jacobs MA, Akhbardeh A. Automated Lung Ultrasound Pulmonary Disease Quantification Using an Unsupervised Machine Learning Technique for COVID-19. Diagnostics (Basel) 2023; 13:2692. [PMID: 37627951 PMCID: PMC10453777 DOI: 10.3390/diagnostics13162692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/30/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
COVID-19 is an ongoing global health pandemic. Although COVID-19 can be diagnosed with various tests such as PCR, these tests do not establish pulmonary disease burden. Whereas point-of-care lung ultrasound (POCUS) can directly assess the severity of characteristic pulmonary findings of COVID-19, the advantage of using US is that it is inexpensive, portable, and widely available for use in many clinical settings. For automated assessment of pulmonary findings, we have developed an unsupervised learning technique termed the calculated lung ultrasound (CLU) index. The CLU can quantify various types of lung findings, such as A or B lines, consolidations, and pleural effusions, and it uses these findings to calculate a CLU index score, which is a quantitative measure of pulmonary disease burden. This is accomplished using an unsupervised, patient-specific approach that does not require training on a large dataset. The CLU was tested on 52 lung ultrasound examinations from several institutions. CLU demonstrated excellent concordance with radiologist findings in different pulmonary disease states. Given the global nature of COVID-19, the CLU would be useful for sonographers and physicians in resource-strapped areas with limited ultrasound training and diagnostic capacities for more accurate assessment of pulmonary status.
Collapse
Affiliation(s)
- Hersh Sagreiya
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael A. Jacobs
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Alireza Akhbardeh
- Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center, Houston, TX 77030, USA
- Ambient Digital LLC, Daly City, CA 94014, USA
| |
Collapse
|
15
|
Pavel I, Ciocoiu IB. COVID-19 Detection from Cough Recordings Using Bag-of-Words Classifiers. SENSORS (BASEL, SWITZERLAND) 2023; 23:4996. [PMID: 37299721 PMCID: PMC10255075 DOI: 10.3390/s23114996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
Reliable detection of COVID-19 from cough recordings is evaluated using bag-of-words classifiers. The effect of using four distinct feature extraction procedures and four different encoding strategies is evaluated in terms of the Area Under Curve (AUC), accuracy, sensitivity, and F1-score. Additional studies include assessing the effect of both input and output fusion approaches and a comparative analysis against 2D solutions using Convolutional Neural Networks. Extensive experiments conducted on the COUGHVID and COVID-19 Sounds datasets indicate that sparse encoding yields the best performances, showing robustness against various combinations of feature type, encoding strategy, and codebook dimension parameters.
Collapse
Affiliation(s)
| | - Iulian B. Ciocoiu
- Faculty of Electronics, Telecommunications and Information Technology, “Gheorghe Asachi” Technical University of Iasi, Bd. Carol I 11A, 700050 Iasi, Romania;
| |
Collapse
|
16
|
Bruno A, Ignesti G, Salvetti O, Moroni D, Martinelli M. Efficient Lung Ultrasound Classification. Bioengineering (Basel) 2023; 10:bioengineering10050555. [PMID: 37237625 DOI: 10.3390/bioengineering10050555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/28/2023] Open
Abstract
A machine learning method for classifying lung ultrasound is proposed here to provide a point of care tool for supporting a safe, fast, and accurate diagnosis that can also be useful during a pandemic such as SARS-CoV-2. Given the advantages (e.g., safety, speed, portability, cost-effectiveness) provided by the ultrasound technology over other examinations (e.g., X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest public lung ultrasound dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art models by at least 5%. The complexity is restrained by adopting specific design choices: ensembling with an adaptive combination layer, ensembling performed on the deep features, and minimal ensemble using two weak models only. In this way, the number of parameters has the same order of magnitude of a single EfficientNet-b0 and the computational cost (FLOPs) is reduced at least by 20%, doubled by parallelization. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where an inaccurate weak model focuses its attention versus an accurate one.
Collapse
Affiliation(s)
- Antonio Bruno
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
| | - Giacomo Ignesti
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
| | - Ovidio Salvetti
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
| | - Davide Moroni
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
| | - Massimo Martinelli
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
| |
Collapse
|
17
|
Jaworska J, Buda N, Kwaśniewicz P, Komorowska-Piotrowska A, Sands D. Lung Ultrasound in the Evaluation of Lung Disease Severity in Children with Clinically Stable Cystic Fibrosis: A Prospective Cross-Sectional Study. J Clin Med 2023; 12:jcm12093086. [PMID: 37176526 PMCID: PMC10179222 DOI: 10.3390/jcm12093086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 04/07/2023] [Accepted: 04/13/2023] [Indexed: 05/15/2023] Open
Abstract
With the increasing longevity of cystic fibrosis (CF), there is a growing need to minimise exposure to ionising radiation in patients who undergo regular imaging tests while monitoring the course of the lung disease. This study aimed to define the role of lung ultrasounds (LUS) in the evaluation of lung disease severity in children with clinically stable CF. LUS was performed on 131 patients aged 5 weeks to 18 years (study group) and in 32 healthy children of an equivalent age range (control group). Additionally, an interobserver study was performed on 38 patients from the study group. In CF patients, the following ultrasound signs were identified: I-lines; Z-lines; single, numerous and confluent B-lines; Am-lines; small and major consolidations; pleural line abnormalities and small amounts of pleural fluid. The obtained results were evaluated against an original ultrasound score. LUS results were correlated with the results of chest X-ray (CXR) [very high], pulmonary function tests (PFTs) [high] and microbiological status [significant]. The interobserver study showed very good agreement between investigators. We conclude that LUS is a useful test in the evaluation of CF lung disease severity compared to routinely used methods. With appropriate standardisation, LUS is highly reproducible.
Collapse
Affiliation(s)
- Joanna Jaworska
- Cystic Fibrosis Department, Institute of Mother and Child, 01-211 Warsaw, Poland
| | - Natalia Buda
- Department of Internal Medicine, Connective Tissue Diseases and Geriatrics, Medical University of Gdansk, 80-214 Gdansk, Poland
| | - Piotr Kwaśniewicz
- Department of Diagnostic Imaging, Institute of Mother and Child, 01-211 Warsaw, Poland
| | | | - Dorota Sands
- Cystic Fibrosis Department, Institute of Mother and Child, 01-211 Warsaw, Poland
| |
Collapse
|
18
|
Zhang S, Liao M, Wang J, Zhu Y, Zhang Y, Zhang J, Zheng R, Lv L, Zhu D, Chen H, Wang W. Fully automatic tumor segmentation of breast ultrasound images with deep learning. J Appl Clin Med Phys 2022; 24:e13863. [PMID: 36495018 PMCID: PMC9859996 DOI: 10.1002/acm2.13863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/28/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Breast ultrasound (BUS) imaging is one of the most prevalent approaches for the detection of breast cancers. Tumor segmentation of BUS images can facilitate doctors in localizing tumors and is a necessary step for computer-aided diagnosis systems. While the majority of clinical BUS scans are normal ones without tumors, segmentation approaches such as U-Net often predict mass regions for these images. Such false-positive problem becomes serious if a fully automatic artificial intelligence system is used for routine screening. METHODS In this study, we proposed a novel model which is more suitable for routine BUS screening. The model contains a classification branch that determines whether the image is normal or with tumors, and a segmentation branch that outlines tumors. Two branches share the same encoder network. We also built a new dataset that contains 1600 BUS images from 625 patients for training and a testing dataset with 130 images from 120 patients for testing. The dataset is the largest one with pixel-wise masks manually segmented by experienced radiologists. Our code is available at https://github.com/szhangNJU/BUS_segmentation. RESULTS The area under the receiver operating characteristic curve (AUC) for classifying images into normal/abnormal categories was 0.991. The dice similarity coefficient (DSC) for segmentation of mass regions was 0.898, better than the state-of-the-art models. Testing on an external dataset gave a similar performance, demonstrating a good transferability of our model. Moreover, we simulated the use of the model in actual clinic practice by processing videos recorded during BUS scans; the model gave very low false-positive predictions on normal images without sacrificing sensitivities for images with tumors. CONCLUSIONS Our model achieved better segmentation performance than the state-of-the-art models and showed a good transferability on an external test set. The proposed deep learning architecture holds potential for use in fully automatic BUS health screening.
Collapse
Affiliation(s)
- Shuai Zhang
- Collaborative Innovation Center of Advanced MicrostructuresSchool of PhysicsNanjing UniversityNanjingChina
| | - Mei Liao
- Department of UltrasoundThird Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Jing Wang
- Department of Radiation OncologyEmory UniversityAtlantaGeorgiaUSA
| | - Yongyi Zhu
- Department of UltrasoundThird Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Yanling Zhang
- Department of UltrasoundThird Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Jian Zhang
- Collaborative Innovation Center of Advanced MicrostructuresSchool of PhysicsNanjing UniversityNanjingChina
- Institute for Brain SciencesNanjing UniversityNanjingChina
| | - Rongqin Zheng
- Department of UltrasoundThird Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | | | | | - Hao Chen
- Precision Care technologyHangzhouChina
| | - Wei Wang
- Collaborative Innovation Center of Advanced MicrostructuresSchool of PhysicsNanjing UniversityNanjingChina
- Institute for Brain SciencesNanjing UniversityNanjingChina
| |
Collapse
|
19
|
Ienghong K, Cheung LW, Tiamkao S, Bhudhisawasdi V, Apiratwarakul K. The diagnostic capabilities of the combined cardiac and lung point of care ultrasound in shocked patients at the emergency department - Resourced limited country. Eur J Radiol Open 2022; 9:100446. [PMID: 36250194 PMCID: PMC9554831 DOI: 10.1016/j.ejro.2022.100446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 11/04/2022] Open
Abstract
PURPOSE Cardiac, lung, and inferior vena cava (IVC) ultrasound are commonly performed in the care of emergency patients especially patient presented with hypotension or shock. However, the literature indicated the limitation of IVC to assess shocked patients. This study aims to determine the efficacy of combined cardiac and lung ultrasound for evaluation the etiology of shock. MATERIALS AND METHODS A cross-sectional study was conducted on patient with shock at emergency department, Srinagarind Hospital, Thailand, from January to December 2021. Adult shocked patients who met the criteria were included in this study. Ultrasound and emergency department medical records were documented and analyzed as sensitivity, specificity, predictive value, negative predictive value, diagnostic accuracy, and Cohen's kappa coefficient (κ). RESULTS One hundred and two who met the criteria were enrolled. Combined cardiac and lung scans were found to be accurate 99.02% and 93.04% in obstructive and cardiogenic shock. In patients with obstructive shock was the almost perfect agreement, (κ) = 0.85. However, distributive, and hypovolemic shock had the low concordance with the final hospital diagnosis, (κ) = 0.37 and 0.43, respectively. CONCLUSIONS The integration of cardiac and lung ultrasound can be effectively used to narrow differential diagnosis of shock.
Collapse
Affiliation(s)
- Kamonwon Ienghong
- Department of Emergency Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Development of integrated Point of Care Ultrasound Used in Emergency Department, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Lap Woon Cheung
- Accident & Emergency Department, Princess Margaret Hospital, Kowloon, Hong Kong
- Emergency Medicine Unit, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Somsak Tiamkao
- Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | | | - Korakot Apiratwarakul
- Department of Emergency Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Development of integrated Point of Care Ultrasound Used in Emergency Department, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| |
Collapse
|