1
|
Khan U, Afrakhteh S, Mento F, Mert G, Smargiassi A, Inchingolo R, Tursi F, Macioce VN, Perrone T, Iacca G, Demi L. Low-complexity lung ultrasound video scoring by means of intensity projection-based video compression. Comput Biol Med 2024; 169:107885. [PMID: 38141447 DOI: 10.1016/j.compbiomed.2023.107885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/27/2023] [Accepted: 12/18/2023] [Indexed: 12/25/2023]
Abstract
Since the outbreak of COVID-19, efforts have been made towards semi-quantitative analysis of lung ultrasound (LUS) data to assess the patient's condition. Several methods have been proposed in this regard, with a focus on frame-level analysis, which was then used to assess the condition at the video and prognostic levels. However, no extensive work has been done to analyze lung conditions directly at the video level. This study proposes a novel method for video-level scoring based on compression of LUS video data into a single image and automatic classification to assess patient's condition. The method utilizes maximum, mean, and minimum intensity projection-based compression of LUS video data over time. This enables to preserve hyper- and hypo-echoic data regions, while compressing the video down to a maximum of three images. The resulting images are then classified using a convolutional neural network (CNN). Finally, the worst predicted score given among the images is assigned to the corresponding video. The results show that this compression technique can achieve a promising agreement at the prognostic level (81.62%), while the video-level agreement remains comparable with the state-of-the-art (46.19%). Conclusively, the suggested method lays down the foundation for LUS video compression, shifting from frame-level to direct video-level analysis of LUS data.
Collapse
Affiliation(s)
- Umair Khan
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Sajjad Afrakhteh
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Federico Mento
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Gizem Mert
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | | | - Tiziano Perrone
- Dipartimento di Emergenza ed Urgenza, Humanitas Gavazzeni Bergamo, Bergamo, Italy
| | - Giovanni Iacca
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
| |
Collapse
|
2
|
Mento F, Perini M, Malacarne C, Demi L. Ultrasound multifrequency strategy to estimate the lung surface roughness, in silico and in vitro results. Ultrasonics 2023; 135:107143. [PMID: 37647701 DOI: 10.1016/j.ultras.2023.107143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/28/2023] [Accepted: 08/21/2023] [Indexed: 09/01/2023]
Abstract
Lung ultrasound (LUS) is an important imaging modality to assess the state of the lung surface. Nevertheless, LUS is limited to the visual evaluation of imaging artifacts, especially the vertical ones. These artifacts are observed in pathologies characterized by a reduction of dimensions of air-spaces (alveoli). In contrast, there exist pathologies, such as chronic obstructive pulmonary disease (COPD), in which an enlargement of air-spaces can occur, which causes the lung surface to behave essentially as a perfect reflector, thus not allowing ultrasound penetration. This characteristic high reflectivity could be exploited to characterize the lung surface. Specifically, air-spaces of different sizes could cause the lung surface to have a different roughness, whose estimation could provide a way to assess the state of the lung surface. In this study, we present a quantitative multifrequency approach aiming at estimating the lung surface's roughness by measuring image intensity variations along the lung surface as a function of frequency. This approach was tested both in silico and in vitro, and it showed promising results. For the in vitro experiments, radiofrequency (RF) data were acquired from a novel experimental model. The results showed consistency between in silico and in vitro experiments.
Collapse
Affiliation(s)
- Federico Mento
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Trento, Italy
| | - Matteo Perini
- Polo Meccatronica (ProM), Via Fortunato Zeni 8, 38068 Rovereto, Italy
| | - Ciro Malacarne
- Polo Meccatronica (ProM), Via Fortunato Zeni 8, 38068 Rovereto, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Trento, Italy.
| |
Collapse
|
3
|
Khan U, Afrakhteh S, Mento F, Fatima N, De Rosa L, Custode LL, Azam Z, Torri E, Soldati G, Tursi F, Macioce VN, Smargiassi A, Inchingolo R, Perrone T, Iacca G, Demi L. Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level. Ultrasonics 2023; 132:106994. [PMID: 37015175 PMCID: PMC10060012 DOI: 10.1016/j.ultras.2023.106994] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 05/29/2023]
Abstract
Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated. This paper takes on these challenges by providing a benchmark analysis of methods from frame to prognostic level. For frame-level scoring, state-of-the-art deep learning models are evaluated with additional analysis of best performing model in transfer-learning settings. A novel cross-correlation based aggregation technique is proposed for video and exam-level scoring. Results showed that ResNet-18, when trained from scratch, outperformed the existing methods with an F1-Score of 0.659. The proposed aggregation method resulted in 59.51%, 63.29%, and 84.90% agreement with clinicians at the video, exam, and prognostic levels, respectively; thus, demonstrating improved performances over the state of the art. It was also found that filtering frames based on the posterior probability shows higher impact on the LUS analysis in comparison to temporal downsampling. All of these analysis were conducted over the largest standardized and clinically validated LUS dataset from COVID-19 patients.
Collapse
Affiliation(s)
- Umair Khan
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Sajjad Afrakhteh
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Federico Mento
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Noreen Fatima
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Laura De Rosa
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Leonardo Lucio Custode
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Zihadul Azam
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Elena Torri
- Dipartimento di Emergenza ed Urgenza, Humanitas Gavazzeni Bergamo, Bergamo, Italy
| | - Gino Soldati
- Diagnostic and Interventional Ultrasound Unit, Valle del Serchio General Hospital, Lucca, Italy
| | | | | | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Tiziano Perrone
- Dipartimento di Emergenza ed Urgenza, Humanitas Gavazzeni Bergamo, Bergamo, Italy
| | - Giovanni Iacca
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
| |
Collapse
|
4
|
Fatima N, Mento F, Zanforlin A, Smargiassi A, Torri E, Perrone T, Demi L. Human-to-AI Interrater Agreement for Lung Ultrasound Scoring in COVID-19 Patients. J Ultrasound Med 2023; 42:843-851. [PMID: 35796343 PMCID: PMC9350219 DOI: 10.1002/jum.16052] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 05/18/2023]
Abstract
OBJECTIVES Lung ultrasound (LUS) has sparked significant interest during COVID-19. LUS is based on the detection and analysis of imaging patterns. Vertical artifacts and consolidations are some of the recognized patterns in COVID-19. However, the interrater reliability (IRR) of these findings has not been yet thoroughly investigated. The goal of this study is to assess IRR in LUS COVID-19 data and determine how many LUS videos and operators are required to obtain a reliable result. METHODS A total of 1035 LUS videos from 59 COVID-19 patients were included. Videos were randomly selected from a dataset of 1807 videos and scored by six human operators (HOs). The videos were also analyzed by artificial intelligence (AI) algorithms. Fleiss' kappa coefficient results are presented, evaluated at both the video and prognostic levels. RESULTS Findings show a stable agreement when evaluating a minimum of 500 videos. The statistical analysis illustrates that, at a video level, a Fleiss' kappa coefficient of 0.464 (95% confidence interval [CI] = 0.455-0.473) and 0.404 (95% CI = 0.396-0.412) is obtained for pairs of HOs and for AI versus HOs, respectively. At prognostic level, a Fleiss' kappa coefficient of 0.505 (95% CI = 0.448-0.562) and 0.506 (95% CI = 0.458-0.555) is obtained for pairs of HOs and for AI versus HOs, respectively. CONCLUSIONS To examine IRR and obtain a reliable evaluation, a minimum of 500 videos are recommended. Moreover, the employed AI algorithms achieve results that are comparable with HOs. This research further provides a methodology that can be useful to benchmark future LUS studies.
Collapse
Affiliation(s)
- Noreen Fatima
- Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
- UltraAITrentoItaly
| | - Federico Mento
- Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
| | | | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Medical and Surgical SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Elena Torri
- Emergency DepartmentHumanitas GavazzeniBergamoItaly
| | - Tiziano Perrone
- Emergency DepartmentHumanitas GavazzeniBergamoItaly
- Department of Internal MedicineIRCCS San Matteo Hospital Foundation, University of PaviaPaviaItaly
| | - Libertario Demi
- Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
| |
Collapse
|
5
|
Custode LL, Mento F, Tursi F, Smargiassi A, Inchingolo R, Perrone T, Demi L, Iacca G. Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees. Appl Soft Comput 2023; 133:109926. [PMID: 36532127 PMCID: PMC9746028 DOI: 10.1016/j.asoc.2022.109926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 10/26/2022] [Accepted: 12/08/2022] [Indexed: 12/15/2022]
Abstract
COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients' conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyze the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.
Collapse
Affiliation(s)
| | - Federico Mento
- Dept. of Information Engineering and Computer Science, University of Trento, Italy
| | | | - Andrea Smargiassi
- Dept. of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Riccardo Inchingolo
- Dept. of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Tiziano Perrone
- Dept. of Internal Medicine, IRCCS San Matteo, Pavia, Italy,Emergency Dept., Humanitas Gavazzeni, Bergamo, Italy
| | - Libertario Demi
- Dept. of Information Engineering and Computer Science, University of Trento, Italy
| | - Giovanni Iacca
- Dept. of Information Engineering and Computer Science, University of Trento, Italy,Corresponding author
| |
Collapse
|
6
|
Mento F, Khan U, Faita F, Smargiassi A, Inchingolo R, Perrone T, Demi L. State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses. Ultrasound Med Biol 2022; 48:2398-2416. [PMID: 36155147 PMCID: PMC9499741 DOI: 10.1016/j.ultrasmedbio.2022.07.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 05/27/2023]
Abstract
Lung ultrasound (LUS) has been increasingly expanding since the 1990s, when the clinical relevance of vertical artifacts was first reported. However, the massive spread of LUS is only recent and is associated with the coronavirus disease 2019 (COVID-19) pandemic, during which semi-quantitative computer-aided techniques were proposed to automatically classify LUS data. In this review, we discuss the state of the art in LUS, from semi-quantitative image analysis approaches to quantitative techniques involving the analysis of radiofrequency data. We also discuss recent in vitro and in silico studies, as well as research on LUS safety. Finally, conclusions are drawn highlighting the potential future of LUS.
Collapse
Affiliation(s)
- Federico Mento
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Umair Khan
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Francesco Faita
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Andrea Smargiassi
- Department of Cardiovascular and Thoracic Sciences, Pulmonary Medicine Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Riccardo Inchingolo
- Department of Cardiovascular and Thoracic Sciences, Pulmonary Medicine Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | - Libertario Demi
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
| |
Collapse
|
7
|
Demi L, Mento F, Di Sabatino A, Fiengo A, Sabatini U, Macioce VN, Robol M, Tursi F, Sofia C, Di Cienzo C, Smargiassi A, Inchingolo R, Perrone T. Lung Ultrasound in COVID-19 and Post-COVID-19 Patients, an Evidence-Based Approach. J Ultrasound Med 2022; 41:2203-2215. [PMID: 34859905 PMCID: PMC9015439 DOI: 10.1002/jum.15902] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/22/2021] [Accepted: 11/19/2021] [Indexed: 05/18/2023]
Abstract
OBJECTIVES Worldwide, lung ultrasound (LUS) was utilized to assess coronavirus disease 2019 (COVID-19) patients. Often, imaging protocols were however defined arbitrarily and not following an evidence-based approach. Moreover, extensive studies on LUS in post-COVID-19 patients are currently lacking. This study analyses the impact of different LUS imaging protocols on the evaluation of COVID-19 and post-COVID-19 LUS data. METHODS LUS data from 220 patients were collected, 100 COVID-19 positive and 120 post-COVID-19. A validated and standardized imaging protocol based on 14 scanning areas and a 4-level scoring system was implemented. We utilized this dataset to compare the capability of 5 imaging protocols, respectively based on 4, 8, 10, 12, and 14 scanning areas, to intercept the most important LUS findings. This to evaluate the optimal trade-off between a time-efficient imaging protocol and an accurate LUS examination. We also performed a longitudinal study, aimed at investigating how to eventually simplify the protocol during follow-up. Additionally, we present results on the agreement between AI models and LUS experts with respect to LUS data evaluation. RESULTS A 12-areas protocol emerges as the optimal trade-off, for both COVID-19 and post-COVID-19 patients. For what concerns follow-up studies, it appears not to be possible to reduce the number of scanning areas. Finally, COVID-19 and post-COVID-19 LUS data seem to show differences capable to confuse AI models that were not trained on post-COVID-19 data, supporting the hypothesis of the existence of LUS patterns specific to post-COVID-19 patients. CONCLUSIONS A 12-areas acquisition protocol is recommended for both COVID-19 and post-COVID-19 patients, also during follow-up.
Collapse
Affiliation(s)
- Libertario Demi
- Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
| | - Federico Mento
- Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
| | - Antonio Di Sabatino
- Department of Internal Medicine, IRCCS San Matteo Hospital FoundationUniversity of PaviaPaviaItaly
| | - Anna Fiengo
- Department of Internal Medicine, IRCCS San Matteo Hospital FoundationUniversity of PaviaPaviaItaly
| | - Umberto Sabatini
- Department of Internal Medicine, IRCCS San Matteo Hospital FoundationUniversity of PaviaPaviaItaly
| | | | - Marco Robol
- Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
| | | | - Carmelo Sofia
- Pulmonary Medicine Unit, Department of Medical and Surgical SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Chiara Di Cienzo
- Pulmonary Medicine Unit, Department of Medical and Surgical SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Medical and Surgical SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Medical and Surgical SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Tiziano Perrone
- Department of Internal Medicine, IRCCS San Matteo Hospital FoundationUniversity of PaviaPaviaItaly
- Emergency DepartmentHumanitas GavazzeniBergamoItaly
| |
Collapse
|
8
|
Khan U, Mento F, Nicolussi Giacomaz L, Trevisan R, Smargiassi A, Inchingolo R, Perrone T, Demi L. Deep Learning-Based Classification of Reduced Lung Ultrasound Data From COVID-19 Patients. IEEE Trans Ultrason Ferroelectr Freq Control 2022; 69:1661-1669. [PMID: 35320098 DOI: 10.1109/tuffc.2022.3161716] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The application of lung ultrasound (LUS) imaging for the diagnosis of lung diseases has recently captured significant interest within the research community. With the ongoing COVID-19 pandemic, many efforts have been made to evaluate LUS data. A four-level scoring system has been introduced to semiquantitatively assess the state of the lung, classifying the patients. Various deep learning (DL) algorithms supported with clinical validations have been proposed to automate the stratification process. However, no work has been done to evaluate the impact on the automated decision by varying pixel resolution and bit depth, leading to the reduction in size of overall data. This article evaluates the performance of DL algorithm over LUS data with varying pixel and gray-level resolution. The algorithm is evaluated over a dataset of 448 LUS videos captured from 34 examinations of 20 patients. All videos are resampled by a factor of 2, 3, and 4 of original resolution, and quantized to 128, 64, and 32 levels, followed by score prediction. The results indicate that the automated scoring shows negligible variation in accuracy when it comes to the quantization of intensity levels only. Combined effect of intensity quantization with spatial down-sampling resulted in a prognostic agreement ranging from 73.5% to 82.3%.These results also suggest that such level of prognostic agreement can be achieved over evaluation of data reduced to 32 times of its original size. Thus, laying foundation to efficient processing of data in resource constrained environments.
Collapse
|
9
|
Frank O, Schipper N, Vaturi M, Soldati G, Smargiassi A, Inchingolo R, Torri E, Perrone T, Mento F, Demi L, Galun M, Eldar YC, Bagon S. Integrating Domain Knowledge Into Deep Networks for Lung Ultrasound With Applications to COVID-19. IEEE Trans Med Imaging 2022; 41:571-581. [PMID: 34606447 PMCID: PMC9014480 DOI: 10.1109/tmi.2021.3117246] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/26/2021] [Accepted: 09/29/2021] [Indexed: 05/18/2023]
Abstract
Lung ultrasound (LUS) is a cheap, safe and non-invasive imaging modality that can be performed at patient bed-side. However, to date LUS is not widely adopted due to lack of trained personnel required for interpreting the acquired LUS frames. In this work we propose a framework for training deep artificial neural networks for interpreting LUS, which may promote broader use of LUS. When using LUS to evaluate a patient's condition, both anatomical phenomena (e.g., the pleural line, presence of consolidations), as well as sonographic artifacts (such as A- and B-lines) are of importance. In our framework, we integrate domain knowledge into deep neural networks by inputting anatomical features and LUS artifacts in the form of additional channels containing pleural and vertical artifacts masks along with the raw LUS frames. By explicitly supplying this domain knowledge, standard off-the-shelf neural networks can be rapidly and efficiently finetuned to accomplish various tasks on LUS data, such as frame classification or semantic segmentation. Our framework allows for a unified treatment of LUS frames captured by either convex or linear probes. We evaluated our proposed framework on the task of COVID-19 severity assessment using the ICLUS dataset. In particular, we finetuned simple image classification models to predict per-frame COVID-19 severity score. We also trained a semantic segmentation model to predict per-pixel COVID-19 severity annotations. Using the combined raw LUS frames and the detected lines for both tasks, our off-the-shelf models performed better than complicated models specifically designed for these tasks, exemplifying the efficacy of our framework.
Collapse
|
10
|
Soldati G, Smargiassi A, Perrone T, Torri E, Mento F, Demi L, Inchingolo R. LUS for COVID-19 Pneumonia: Flexible or Reproducible Approach? J Ultrasound Med 2022; 41:525-526. [PMID: 33885169 PMCID: PMC8250952 DOI: 10.1002/jum.15726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/11/2021] [Indexed: 06/01/2023]
Affiliation(s)
- Gino Soldati
- Diagnostic and Interventional Ultrasound UnitValle del Serchio General HospitalLuccaItaly
| | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Medical and Surgical SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Tiziano Perrone
- Department of Internal Medicine and Therapeutics, Fondazione IRCCS Policlinico San MatteoUniversity of PaviaPaviaItaly
| | - Elena Torri
- Emergency DepartmentHumanitas GavazzeniBergamoItaly
| | - Federico Mento
- Department of Information Engineering and Computer Science, Ultrasound Laboratory TrentoUniversity of TrentoTrentoItaly
| | - Libertario Demi
- Department of Information Engineering and Computer Science, Ultrasound Laboratory TrentoUniversity of TrentoTrentoItaly
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Medical and Surgical SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| |
Collapse
|
11
|
Mento F, Demi L. Dependence of lung ultrasound vertical artifacts on frequency, bandwidth, focus and angle of incidence: An in vitro study. J Acoust Soc Am 2021; 150:4075. [PMID: 34972265 DOI: 10.1121/10.0007482] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/05/2021] [Indexed: 06/14/2023]
Abstract
Lung ultrasound (LUS) is nowadays widely adopted by clinicians to evaluate the state of the lung surface. However, being mainly based on the evaluation of vertical artifacts, whose genesis is still unclear, LUS is affected by qualitative and subjective analyses. Even though semi-quantitative approaches supported by computer aided methods can reduce subjectivity, they do not consider the dependence of vertical artifacts on imaging parameters, and could not be classified as fully quantitative. They are indeed mainly based on scoring LUS images, reconstructed with standard clinical scanners, through the sole evaluation of visual patterns, whose visualization depends on imaging parameters. To develop quantitative techniques is therefore fundamental to understand which parameters influence the vertical artifacts' intensity. In this study, we quantitatively analyzed the dependence of nine vertical artifacts observed in a thorax phantom on four parameters, i.e., center frequency, focal point, bandwidth, and angle of incidence. The results showed how the vertical artifacts are significantly affected by these four parameters, and confirm that the center frequency is the most impactful parameter in artifacts' characterization. These parameters should hence be carefully considered when developing a LUS quantitative approach.
Collapse
Affiliation(s)
- Federico Mento
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123, Trento, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123, Trento, Italy
| |
Collapse
|
12
|
Soldati G, Smargiassi A, Perrone T, Torri E, Mento F, Demi L, Inchingolo R. There is a Validated Acquisition Protocol for Lung Ultrasonography in COVID-19 Pneumonia. J Ultrasound Med 2021; 40:2783. [PMID: 33555606 PMCID: PMC8013676 DOI: 10.1002/jum.15649] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 12/17/2020] [Indexed: 05/05/2023]
Affiliation(s)
- Gino Soldati
- Diagnostic and Interventional Ultrasound UnitValle del Serchio General HospitalLuccaItaly
| | - Andrea Smargiassi
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCSPulmonary Medicine UnitRomeItaly
| | - Tiziano Perrone
- Department of Internal Medicine and TherapeuticsFondazione IRCCS Policlinico San Matteo, University of PaviaPaviaItaly
| | - Elena Torri
- Emergency DepartmentHumanitas GavazzeniBergamoItaly
| | - Federico Mento
- Department of Information Engineering and Computer ScienceUltrasound Laboratory Trento, University of TrentoTrentoItaly
| | - Libertario Demi
- Department of Information Engineering and Computer ScienceUltrasound Laboratory Trento, University of TrentoTrentoItaly
| | - Riccardo Inchingolo
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCSPulmonary Medicine UnitRomeItaly
| |
Collapse
|
13
|
Roshankhah R, Karbalaeisadegh Y, Greer H, Mento F, Soldati G, Smargiassi A, Inchingolo R, Torri E, Perrone T, Aylward S, Demi L, Muller M. Investigating training-test data splitting strategies for automated segmentation and scoring of COVID-19 lung ultrasound images. J Acoust Soc Am 2021; 150:4118. [PMID: 34972274 PMCID: PMC8684042 DOI: 10.1121/10.0007272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 09/21/2021] [Accepted: 09/23/2021] [Indexed: 05/18/2023]
Abstract
Ultrasound in point-of-care lung assessment is becoming increasingly relevant. This is further reinforced in the context of the COVID-19 pandemic, where rapid decisions on the lung state must be made for staging and monitoring purposes. The lung structural changes due to severe COVID-19 modify the way ultrasound propagates in the parenchyma. This is reflected by changes in the appearance of the lung ultrasound images. In abnormal lungs, vertical artifacts known as B-lines appear and can evolve into white lung patterns in the more severe cases. Currently, these artifacts are assessed by trained physicians, and the diagnosis is qualitative and operator dependent. In this article, an automatic segmentation method using a convolutional neural network is proposed to automatically stage the progression of the disease. 1863 B-mode images from 203 videos obtained from 14 asymptomatic individual,14 confirmed COVID-19 cases, and 4 suspected COVID-19 cases were used. Signs of lung damage, such as the presence and extent of B-lines and white lung areas, are manually segmented and scored from zero to three (most severe). These manually scored images are considered as ground truth. Different test-training strategies are evaluated in this study. The results shed light on the efficient approaches and common challenges associated with automatic segmentation methods.
Collapse
Affiliation(s)
- Roshan Roshankhah
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27606, USA
| | | | | | - Federico Mento
- Ultrasound Laboratory, University of Trento, Trento, Italy
| | - Gino Soldati
- Azienda USL Toscana nord ovest Sede di Lucca, Diagnostic and Interventional Ultrasound Unit Lucca, Toscana, Italy
| | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS. Roma, Lazio, Italy
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS. Roma, Lazio, Italy
| | | | - Tiziano Perrone
- Department of Internal Medicine, Istituto di Ricovero e Cura a Carattere Scientifico, San Matteo, Pavia, Italy
| | | | | | - Marie Muller
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27606, USA
| |
Collapse
|
14
|
Mento F, Perrone T, Macioce VN, Tursi F, Buonsenso D, Torri E, Smargiassi A, Inchingolo R, Soldati G, Demi L. On the Impact of Different Lung Ultrasound Imaging Protocols in the Evaluation of Patients Affected by Coronavirus Disease 2019: How Many Acquisitions Are Needed? J Ultrasound Med 2021; 40:2235-2238. [PMID: 33231895 PMCID: PMC7753797 DOI: 10.1002/jum.15580] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 10/27/2020] [Indexed: 05/05/2023]
Abstract
Lung ultrasound (LUS) is currently being extensively used for the evaluation of patients affected by coronavirus disease 2019. In the past months, several imaging protocols have been proposed in the literature. However, how the different protocols would compare when applied to the same patients had not been investigated yet. To this end, in this multicenter study, we analyzed the outcomes of 4 different LUS imaging protocols, respectively based on 4, 8, 12, and 14 LUS acquisitions, on data from 88 patients. Results show how a 12-area acquisition system seems to be a good tradeoff between the acquisition time and accuracy.
Collapse
Affiliation(s)
- Federico Mento
- Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
| | - Tiziano Perrone
- Department of Internal MedicineIstituto di Ricovero e Cura a Carattere Scientifico San MatteoPaviaItaly
| | | | - Francesco Tursi
- Division of Respiratory DiseasesOspedale Maggiore di LodiLodiItaly
| | - Danilo Buonsenso
- Department of Woman and Child Health and Public HealthPoliclinico Universitario Agostino GemelliRomeItaly
| | | | - Andrea Smargiassi
- Respiratory Medicine Unit, Department of Medical and Surgical SciencesPoliclinico Universitario Agostino GemelliRomeItaly
| | - Riccardo Inchingolo
- Respiratory Medicine Unit, Department of Medical and Surgical SciencesPoliclinico Universitario Agostino GemelliRomeItaly
| | - Gino Soldati
- Diagnostic and Interventional Ultrasound UnitValle del Serchio Hospital DistrictLuccaItaly
| | - Libertario Demi
- Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
| |
Collapse
|
15
|
Mento F, Perrone T, Fiengo A, Tursi F, Macioce VN, Smargiassi A, Inchingolo R, Demi L. Limiting the areas inspected by lung ultrasound leads to an underestimation of COVID-19 patients' condition. Intensive Care Med 2021; 47:811-812. [PMID: 33974109 PMCID: PMC8111857 DOI: 10.1007/s00134-021-06407-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Federico Mento
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Tiziano Perrone
- Department of Internal Medicine, IRCCS San Matteo, Pavia, Italy
| | - Anna Fiengo
- Department of Internal Medicine, IRCCS San Matteo, Pavia, Italy
| | | | | | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
| |
Collapse
|
16
|
Mento F, Perrone T, Fiengo A, Smargiassi A, Inchingolo R, Soldati G, Demi L. Deep learning applied to lung ultrasound videos for scoring COVID-19 patients: A multicenter study. J Acoust Soc Am 2021; 149:3626. [PMID: 34241100 DOI: 10.1121/10.0004855] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
In the current pandemic, lung ultrasound (LUS) played a useful role in evaluating patients affected by COVID-19. However, LUS remains limited to the visual inspection of ultrasound data, thus negatively affecting the reliability and reproducibility of the findings. Moreover, many different imaging protocols have been proposed, most of which lacked proper clinical validation. To address these problems, we were the first to propose a standardized imaging protocol and scoring system. Next, we developed the first deep learning (DL) algorithms capable of evaluating LUS videos providing, for each video-frame, the score as well as semantic segmentation. Moreover, we have analyzed the impact of different imaging protocols and demonstrated the prognostic value of our approach. In this work, we report on the level of agreement between the DL and LUS experts, when evaluating LUS data. The results show a percentage of agreement between DL and LUS experts of 85.96% in the stratification between patients at high risk of clinical worsening and patients at low risk. These encouraging results demonstrate the potential of DL models for the automatic scoring of LUS data, when applied to high quality data acquired accordingly to a standardized imaging protocol.
Collapse
Affiliation(s)
- Federico Mento
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123, Trento, Italy
| | - Tiziano Perrone
- Department of Internal Medicine, IRCCS San Matteo, 27100, Pavia, Italy
| | - Anna Fiengo
- Department of Internal Medicine, IRCCS San Matteo, 27100, Pavia, Italy
| | - Andrea Smargiassi
- Department of Cardiovascular and Thoracic Sciences, Pulmonary Medicine Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Riccardo Inchingolo
- Department of Cardiovascular and Thoracic Sciences, Pulmonary Medicine Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Gino Soldati
- Diagnostic and Interventional Ultrasound Unit, Valle del Serchio General Hospital, 55032 Lucca, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123, Trento, Italy
| |
Collapse
|
17
|
Peschiera E, Mento F, Demi L. Numerical study on lung ultrasound B-line formation as a function of imaging frequency and alveolar geometries. J Acoust Soc Am 2021; 149:2304. [PMID: 33940883 DOI: 10.1121/10.0003930] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/04/2021] [Indexed: 06/12/2023]
Abstract
Lung ultrasound (LUS) has become a widely adopted diagnostic method for several lung diseases. However, the presence of air inside the lung does not allow the anatomical investigation of the organ. Therefore, LUS is mainly based on the interpretation of vertical imaging artifacts, called B-lines. These artifacts correlate with several pathologies, but their genesis is still partly unknown. Within this framework, this study focuses on the factors affecting the artifacts' formation by numerically simulating the ultrasound propagation within the lungs through the toolbox k-Wave. Since the main hypothesis behind the generation of B-lines relies on multiple scattering phenomena occurring once acoustic channels open at the lung surface, the impact of changing alveolar size and spacing is of interest. The tested domain is of size 4 cm × 1.6 cm, the investigated frequencies vary from 1 to 5 MHz, and the explored alveolar diameters and spacing range from 100 to 400 μm and from 20 to 395 μm, respectively. Results show the strong and entangled relation among the wavelength, the domain geometries, and the artifact visualization, allowing for better understanding of propagation in such a complex medium and opening several possibilities for future studies.
Collapse
Affiliation(s)
- Emanuele Peschiera
- Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Via Sommarive 9, 38123 Trento, Italy
| | - Federico Mento
- Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Via Sommarive 9, 38123 Trento, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Via Sommarive 9, 38123 Trento, Italy
| |
Collapse
|
18
|
Smargiassi A, Soldati G, Torri E, Mento F, Milardi D, Del Giacomo P, De Matteis G, Burzo ML, Larici AR, Pompili M, Demi L, Inchingolo R. Lung Ultrasound for COVID-19 Patchy Pneumonia: Extended or Limited Evaluations? J Ultrasound Med 2021; 40:521-528. [PMID: 32815618 DOI: 10.1002/jum.15428] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 06/04/2020] [Accepted: 06/23/2020] [Indexed: 05/11/2023]
Abstract
OBJECTIVES The 2019 novel coronavirus (severe acute respiratory syndrome coronavirus 2) is causing cases of severe pneumonia. Lung ultrasound (LUS) could be a useful tool for physicians detecting a bilateral heterogeneous patchy distribution of pathologic findings in a symptomatic suggestive context. The aim of this study was to focus on the implications of limiting LUS examinations to specific regions of the chest. METHODS Patients were evaluated with a standard sequence of LUS scans in 14 anatomic areas. A scoring system of LUS findings was reported, ranging from 0 to 3 (worst score, 3). The scores reported on anterior, lateral, and posterior landmarks were analyzed separately and compared with each other and with the global findings. RESULTS Thirty-eight patients were enrolled. A higher prevalence of score 0 was observed in the anterior region (44.08%). On the contrary, 21.05% of posterior regions and 13.62% of lateral regions were evaluated as score 3, whereas only 5.92% of anterior regions were classified as score 3. Findings from chest computed tomography performed in 16 patients with coronavirus disease 2019 correlated with and matched the distribution of findings from LUS. CONCLUSIONS To assess the quantity and severity of lung disease, a comprehensive LUS examination is recommended. Omitting areas of the chest misses involved lung.
Collapse
Affiliation(s)
- Andrea Smargiassi
- Pulmonary Medicine Unit, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Gino Soldati
- Diagnostic and Interventional Ultrasound Unit, Valle del Serchio General Hospital, Lucca, Italy
| | | | - Federico Mento
- Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, Italy
| | - Domenico Milardi
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Paola Del Giacomo
- Unità Operativa Complessa Malattie Infettive, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Giuseppe De Matteis
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Maria Livia Burzo
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Anna Rita Larici
- Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
- University Department of Radiological and Hematological Sciences, Section of Radiology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Maurizio Pompili
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, Italy
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
- Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| |
Collapse
|
19
|
Soldati G, Smargiassi A, Inchingolo R, Buonsenso D, Perrone T, Briganti DF, Perlini S, Torri E, Mariani A, Mossolani EE, Tursi F, Mento F, Demi L. Time for a new international evidence-based recommendations for point-of-care lung ultrasound. J Ultrasound Med 2021; 40:433-434. [PMID: 32770771 DOI: 10.1002/jum.15412] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 06/09/2020] [Indexed: 06/11/2023]
Affiliation(s)
- Gino Soldati
- Diagnostic and Interventional Ultrasound Unit, Valle del Serchio General Hospital, Lucca, Italy
| | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Danilo Buonsenso
- Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Tiziano Perrone
- Emergency Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Policlinico San Matteo, and Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy
| | - Domenica Federica Briganti
- Emergency Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Policlinico San Matteo, and Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy
| | - Stefano Perlini
- Emergency Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Policlinico San Matteo, and Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy
| | | | | | | | | | - Federico Mento
- Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, Italy
| |
Collapse
|
20
|
Mento F, Soldati G, Prediletto R, Demi M, Demi L. Quantitative Lung Ultrasound Spectroscopy Applied to the Diagnosis of Pulmonary Fibrosis: The First Clinical Study. IEEE Trans Ultrason Ferroelectr Freq Control 2020; 67:2265-2273. [PMID: 32746228 DOI: 10.1109/tuffc.2020.3012289] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The application of ultrasound imaging to the diagnosis of lung diseases is nowadays receiving growing interest. However, lung ultrasound (LUS) is mainly limited to the analysis of imaging artifacts, such as B-lines, which correlate with a wide variety of diseases. Therefore, the results of LUS investigations remain qualitative and subjective, and specificity is obviously suboptimal. Focusing on the development of a quantitative method dedicated to the lung, in this work, we present the first clinical results obtained with quantitative LUS spectroscopy when applied to the differentiation of pulmonary fibrosis. A previously developed specific multifrequency ultrasound imaging technique was utilized to acquire ultrasound images from 26 selected patients. The multifrequency imaging technique was implemented on the ULtrasound Advanced Open Platform (ULA-OP) platform and an LA533 (Esaote, Florence, Italy) linear-array probe was utilized. RF data obtained at different imaging frequencies (3, 4, 5, and 6 MHz) were acquired and processed in order to characterize B-lines based on their frequency content. In particular, B-line native frequencies (the frequency at which a B-line exhibits the highest intensity) and bandwidth (the range of frequencies over which a B-line shows intensities within -6 dB from its highest intensity), as well as B-line intensity, were analyzed. The results show how the analysis of these features allows (in this group of patients) the differentiation of fibrosis with a sensitivity and specificity equal to 92% and 92%, respectively. These promising results strongly motivate toward the extension of the clinical study, aiming at analyzing a larger cohort of patients and including a broader range of pathologies.
Collapse
|
21
|
Carrer L, Donini E, Marinelli D, Zanetti M, Mento F, Torri E, Smargiassi A, Inchingolo R, Soldati G, Demi L, Bovolo F, Bruzzone L. Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data. IEEE Trans Ultrason Ferroelectr Freq Control 2020; 67:2207-2217. [PMID: 32746195 PMCID: PMC8544930 DOI: 10.1109/tuffc.2020.3005512] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/23/2020] [Indexed: 05/18/2023]
Abstract
Recent works highlighted the significant potential of lung ultrasound (LUS) imaging in the management of subjects affected by COVID-19. In general, the development of objective, fast, and accurate automatic methods for LUS data evaluation is still at an early stage. This is particularly true for COVID-19 diagnostic. In this article, we propose an automatic and unsupervised method for the detection and localization of the pleural line in LUS data based on the hidden Markov model and Viterbi Algorithm. The pleural line localization step is followed by a supervised classification procedure based on the support vector machine (SVM). The classifier evaluates the healthiness level of a patient and, if present, the severity of the pathology, i.e., the score value for each image of a given LUS acquisition. The experiments performed on a variety of LUS data acquired in Italian hospitals with both linear and convex probes highlight the effectiveness of the proposed method. The average overall accuracy in detecting the pleura is 84% and 94% for convex and linear probes, respectively. The accuracy of the SVM classification in correctly evaluating the severity of COVID-19 related pleural line alterations is about 88% and 94% for convex and linear probes, respectively. The results as well as the visualization of the detected pleural line and the predicted score chart, provide a significant support to medical staff for further evaluating the patient condition.
Collapse
Affiliation(s)
- Leonardo Carrer
- Department of Information Engineering and Computer ScienceUniversity of Trento38123TrentoItaly
| | - Elena Donini
- Center for Information and Communication TechnologyFondazione Bruno Kessler38123TrentoItaly
| | - Daniele Marinelli
- Department of Information Engineering and Computer ScienceUniversity of Trento38123TrentoItaly
| | - Massimo Zanetti
- Center for Information and Communication TechnologyFondazione Bruno Kessler38123TrentoItaly
| | - Federico Mento
- Department of Information Engineering and Computer ScienceUniversity of Trento38123TrentoItaly
| | | | - Andrea Smargiassi
- Department of Cardiovascular and Thoracic SciencesPulmonary Medicine UnitFondazione Policlinico Universitario Agostino Gemelli IRCCS00168RomeItaly
| | - Riccardo Inchingolo
- Department of Cardiovascular and Thoracic SciencesPulmonary Medicine UnitFondazione Policlinico Universitario Agostino Gemelli IRCCS00168RomeItaly
| | - Gino Soldati
- Diagnostic and Interventional Ultrasound UnitValle del Serchio General Hospital55032LuccaItaly
| | - Libertario Demi
- Department of Information Engineering and Computer ScienceUniversity of Trento38123TrentoItaly
| | - Francesca Bovolo
- Center for Information and Communication TechnologyFondazione Bruno Kessler38123TrentoItaly
| | - Lorenzo Bruzzone
- Department of Information Engineering and Computer ScienceUniversity of Trento38123TrentoItaly
| |
Collapse
|
22
|
Soldati G, Smargiassi A, Inchingolo R, Buonsenso D, Perrone T, Briganti DF, Perlini S, Torri E, Mariani A, Mossolani EE, Tursi F, Mento F, Demi L. On Lung Ultrasound Patterns Specificity in the Management of COVID-19 Patients. J Ultrasound Med 2020; 39:2283-2284. [PMID: 32383781 PMCID: PMC7267146 DOI: 10.1002/jum.15326] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 04/14/2020] [Indexed: 05/22/2023]
Affiliation(s)
- Gino Soldati
- Diagnostic and Interventional Ultrasound UnitValle del Serchio General HospitalLuccaItaly
| | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Cardiovascular and Thoracic SciencesFondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere ScientificoRomeItaly
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Cardiovascular and Thoracic SciencesFondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere ScientificoRomeItaly
| | - Danilo Buonsenso
- Department of Woman and Child Health and Public HealthFondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere ScientificoRomeItaly
| | - Tiziano Perrone
- Emergency Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Policlinico San Matteo, and Department of Internal Medicine and TherapeuticsUniversity of PaviaPaviaItaly
| | - Domenica Federica Briganti
- Emergency Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Policlinico San Matteo, and Department of Internal Medicine and TherapeuticsUniversity of PaviaPaviaItaly
| | - Stefano Perlini
- Emergency Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Policlinico San Matteo, and Department of Internal Medicine and TherapeuticsUniversity of PaviaPaviaItaly
| | | | | | | | | | - Federico Mento
- Department of Information Engineering and Computer Science, Ultrasound Laboratory TrentoUniversity of TrentoTrentoItaly
| | - Libertario Demi
- Department of Information Engineering and Computer Science, Ultrasound Laboratory TrentoUniversity of TrentoTrentoItaly
| |
Collapse
|
23
|
Roy S, Menapace W, Oei S, Luijten B, Fini E, Saltori C, Huijben I, Chennakeshava N, Mento F, Sentelli A, Peschiera E, Trevisan R, Maschietto G, Torri E, Inchingolo R, Smargiassi A, Soldati G, Rota P, Passerini A, van Sloun RJG, Ricci E, Demi L. Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound. IEEE Trans Med Imaging 2020; 39:2676-2687. [PMID: 32406829 DOI: 10.1109/tmi.2020.2994459] [Citation(s) in RCA: 202] [Impact Index Per Article: 50.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.
Collapse
|
24
|
Mento F, Demi L. On the influence of imaging parameters on lung ultrasound B-line artifacts, in vitro study. J Acoust Soc Am 2020; 148:975. [PMID: 32873037 DOI: 10.1121/10.0001797] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 08/06/2020] [Indexed: 05/18/2023]
Abstract
The clinical relevance of lung ultrasonography (LUS) has been rapidly growing since the 1990s. However, LUS is mainly based on the evaluation of visual artifacts (also called B-lines), leading to subjective and qualitative diagnoses. The formation of B-lines remains unknown and, hence, researchers need to study their origin to allow clinicians to quantitatively evaluate the state of lungs. This paper investigates an ambiguity about the formation of B-lines, leading to the formulation of two main hypotheses. The first hypothesis states that the visualization of these artifacts is linked only to the dimension of the emitted beam, whereas the second associates their appearance to specific resonance phenomena. To verify these hypotheses, the frequency spectrum of B-lines was studied by using dedicated lung-phantoms. A research programmable platform connected to an LA533 linear array probe was exploited both to implement a multifrequency approach and to acquire raw radio frequency data. The strength of each artifact was measured as a function of frequency, focal point, and transmitting aperture by means of the artifact total intensity. The results show that the main parameter that influences the visualization of B-lines is the frequency rather than the focal point or the number of transmitting elements.
Collapse
Affiliation(s)
- Federico Mento
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, Trento, 38123, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, Trento, 38123, Italy
| |
Collapse
|
25
|
Soldati G, Smargiassi A, Inchingolo R, Buonsenso D, Perrone T, Briganti DF, Perlini S, Torri E, Mariani A, Mossolani EE, Tursi F, Mento F, Demi L. Is There a Role for Lung Ultrasound During the COVID-19 Pandemic? J Ultrasound Med 2020; 39:1459-1462. [PMID: 32198775 PMCID: PMC7228238 DOI: 10.1002/jum.15284] [Citation(s) in RCA: 293] [Impact Index Per Article: 73.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 03/17/2020] [Indexed: 05/02/2023]
Affiliation(s)
- Gino Soldati
- Diagnostic and Interventional Ultrasound Unit, Valle del Serchio General Hospital, Lucca, Italy
| | - Andrea Smargiassi
- Pulmonary Medicine Unit, Department of Cardiovascular and Thoracic Sciences, Scientifico, Rome, Italy
| | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Cardiovascular and Thoracic Sciences, Scientifico, Rome, Italy
| | - Danilo Buonsenso
- Department of Woman and Child Health and Public Health, Scientifico, Rome, Italy
| | - Tiziano Perrone
- Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
- Emergency Department, Fondazione Policlinico San Matteo, Istituto di Ricovero e Cura a Carattere Scientifico, and Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy
| | - Domenica Federica Briganti
- Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
- Emergency Department, Fondazione Policlinico San Matteo, Istituto di Ricovero e Cura a Carattere Scientifico, and Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy
| | - Stefano Perlini
- Fondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
- Emergency Department, Fondazione Policlinico San Matteo, Istituto di Ricovero e Cura a Carattere Scientifico, and Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy
| | | | | | | | | | - Federico Mento
- Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, Italy
| | - Libertario Demi
- Department of Information Engineering and Computer Science, Ultrasound Laboratory Trento, University of Trento, Trento, Italy
| |
Collapse
|
26
|
Soldati G, Smargiassi A, Inchingolo R, Buonsenso D, Perrone T, Briganti DF, Perlini S, Torri E, Mariani A, Mossolani EE, Tursi F, Mento F, Demi L. Proposal for International Standardization of the Use of Lung Ultrasound for Patients With COVID-19: A Simple, Quantitative, Reproducible Method. J Ultrasound Med 2020; 39:1413-1419. [PMID: 32227492 PMCID: PMC7228287 DOI: 10.1002/jum.15285] [Citation(s) in RCA: 380] [Impact Index Per Article: 95.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 03/23/2020] [Accepted: 03/23/2020] [Indexed: 05/02/2023]
Abstract
Growing evidence is showing the usefulness of lung ultrasound in patients with the 2019 new coronavirus disease (COVID-19). Severe acute respiratory syndrome coronavirus 2 has now spread in almost every country in the world. In this study, we share our experience and propose a standardized approach to optimize the use of lung ultrasound in patients with COVID-19. We focus on equipment, procedure, classification, and data sharing.
Collapse
Affiliation(s)
- Gino Soldati
- Diagnostic and Interventional Ultrasound UnitValle del Serchio General HospitalLuccaItaly
| | | | - Riccardo Inchingolo
- Pulmonary Medicine Unit, Department of Cardiovascular and Thoracic SciencesFondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere ScientificoRomeItaly
| | - Danilo Buonsenso
- Department of Woman and Child Health and Public HealthFondazione Policlinico Universitario Agostino Gemelli, Istituto di Ricovero e Cura a Carattere ScientificoRomeItaly
| | | | - Domenica Federica Briganti
- Emergency Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Policlinico San Matteo, and Department of Internal Medicine and TherapeuticsUniversity of PaviaPaviaItaly
| | - Stefano Perlini
- Emergency Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Policlinico San Matteo, and Department of Internal Medicine and TherapeuticsUniversity of PaviaPaviaItaly
| | | | | | | | | | - Federico Mento
- Department of Information Engineering and Computer Science, Ultrasound Laboratory TrentoUniversity of TrentoTrentoItaly
| | - Libertario Demi
- Department of Information Engineering and Computer Science, Ultrasound Laboratory TrentoUniversity of TrentoTrentoItaly
| |
Collapse
|
27
|
Micali B, Mento F, Gioffrè Micali M, Venuti A, Saitta E. [Papillo-sphincterotomy in the treatment of lithiasis of the principal bile duct and of non-neoplastic stenosis of the papilla]. MINERVA CHIR 1980; 35:25-9. [PMID: 7393459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Personal experience acquired in a series of 130 papillo-sphincterotomies carried out over a five-year period for the treatment of lithiasis of the VBP and benign stenosis of the papilla is reported. An account is given of the literature data showing that his method, which is comparable with biliodigestive anastomosis techniques, is safe and reliable. Attention is also drawn to its undoubted advantages in the prevention of some serious late complications.
Collapse
|