scholarly journals B-line quantification: comparing learners novice to lung ultrasound assisted by machine artificial intelligence technology to expert review

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Frances M. Russell ◽  
Robert R. Ehrman ◽  
Allen Barton ◽  
Elisa Sarmiento ◽  
Jakob E. Ottenhoff ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Quantitative Analysis ◽  
Image Quality ◽  
Lung Ultrasound ◽  
Learning Algorithm ◽  
Intraclass Correlation ◽  
Training Session ◽  
Expert Review ◽  
Novice Learners ◽  
B Lines

Abstract Background The goal of this study was to assess the ability of machine artificial intelligence (AI) to quantitatively assess lung ultrasound (LUS) B-line presence using images obtained by learners novice to LUS in patients with acute heart failure (AHF), compared to expert interpretation. Methods This was a prospective, multicenter observational study conducted at two urban academic institutions. Learners novice to LUS completed a 30-min training session on lung image acquisition which included lecture and hands-on patient scanning. Learners independently acquired images on patients with suspected AHF. Automatic B-line quantification was obtained offline after completion of the study. Machine AI counted the maximum number of B-lines visualized during a clip. The criterion standard for B-line counts was semi-quantitative analysis by a blinded point-of-care LUS expert reviewer. Image quality was blindly determined by an expert reviewer. A second expert reviewer blindly determined B-line counts and image quality. Intraclass correlation was used to determine agreement between machine AI and expert, and expert to expert. Results Fifty-one novice learners completed 87 scans on 29 patients. We analyzed data from 611 lung zones. The overall intraclass correlation for agreement between novice learner images post-processed with AI technology and expert review was 0.56 (confidence interval [CI] 0.51–0.62), and 0.82 (CI 0.73–0.91) between experts. Median image quality was 4 (on a 5-point scale), and correlation between experts for quality assessment was 0.65 (CI 0.48–0.82). Conclusion After a short training session, novice learners were able to obtain high-quality images. When the AI deep learning algorithm was applied to those images, it quantified B-lines with moderate-to-fair correlation as compared to semi-quantitative analysis by expert review. This data shows promise, but further development is needed before widespread clinical use.

scholarly journals Artificial Intelligence and Democratization of the Use of Lung Ultrasound in COVID-19: On the Feasibility of Automatic Calculation of Lung Ultrasound Score

2022 ◽  
Vol 2 (1) ◽  
pp. 17-25
Author(s):  
Jorge Camacho ◽  
Mario Muñoz ◽  
Vicente Genovés ◽  
Joaquín L. Herraiz ◽  
Ignacio Ortega ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Lung Ultrasound ◽  
Detection Algorithm ◽  
Image Processing Algorithm ◽  
Automatic Calculation ◽  
Ultrasound Score ◽  
Experienced Physician ◽  
Aided Diagnosis ◽  
B Lines

During the COVID-19 pandemic, lung ultrasound has been revealed as a powerful technique for diagnosis and follow-up of pneumonia, the principal complication of SARS-CoV-2 infection. Nevertheless, being a relatively new and unknown technique, the lack of trained personnel has limited its application worldwide. Computer-aided diagnosis could possibly help to reduce the learning curve for less experienced physicians, and to extend such a new technique such as lung ultrasound more quickly. This work presents the preliminary results of the ULTRACOV (Ultrasound in Coronavirus disease) study, aimed to explore the feasibility of a real-time image processing algorithm for automatic calculation of the lung ultrasound score (LUS). A total of 28 patients positive on COVID-19 were recruited and scanned in 12 thorax zones following the lung score protocol, saving a 3 s video at each probe position. Those videos were evaluated by an experienced physician and by a custom developed automated detection algorithm, looking for A-Lines, B-Lines, consolidations, and pleural effusions. The agreement between the findings of the expert and the algorithm was 88.0% for B-Lines, 93.4% for consolidations and 99.7% for pleural effusion detection, and 72.8% for the individual video score. The standard deviation of the patient lung score difference between the expert and the algorithm was ±2.2 points over 36. The exam average time with the ULTRACOV prototype was 5.3 min, while with a conventional scanner was 12.6 min. Conclusion: A good agreement between the algorithm output and an experienced physician was observed, which is a first step on the feasibility of developing a real-time aided-diagnosis lung ultrasound equipment. Additionally, the examination time was reduced to less than half with regard to a conventional ultrasound exam. Acquiring a complete lung ultrasound exam within a few minutes is possible using fairly simple ultrasound machines that are enhanced with artificial intelligence, such as the one we propose. This step is critical to democratize the use of lung ultrasound in these difficult times.

scholarly journals Value of bowel preparation techniques for prostate MRI: a preliminary study

2021 ◽  
Author(s):  
Cynthia Schmidt ◽  
Andreas M. Hötker ◽  
Urs J. Muehlematter ◽  
Irene A. Burger ◽  
Olivio F. Donati ◽  
...  
Keyword(s):  
Image Quality ◽  
Bowel Preparation ◽  
Statistical Tests ◽  
Intraclass Correlation ◽  
Geometric Distortion ◽  
Significant Independent Predictor ◽  
Contrast Enhanced ◽  
Treatment Naïve ◽  
Urogenital Imaging ◽  
Study Type

Abstract Background Bowel preparation before multiparametric MRI (mpMRI) of the prostate is performed widely, despite contradictory or no evidence for efficacy. Purpose To investigate the value of hyoscine N-butylbromide (HBB), microenema (ME) and ‘dietary restrictions’ (DR) for artifact reduction and image quality (IQ) in mpMRI of the prostate. Study type Retrospective. Population Between 10/2018 and 02/2020 treatment-naïve men (median age, 64.9; range 39.8–87.3) who underwent mpMRI of the prostate were included. The total patient sample comprised of n = 180 patients, who received either HBB, ME, were instructed to adhere to DR, or received a combination of those measures prior to the MR scan. Field strength/sequence T2-weighted imaging (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI) scanned on two 3T systems. Assessment A radiologist specialized in urogenital imaging (R1) and a senior radiology resident (R2) visually assessed IQ parameters on transversal T2w, DWI and ADC maps on a 5-point Likert-like scale. Statistical tests Group comparison between IQ parameters was performed on reader level using Kruskal–Wallis and Mann–Whitney U tests. Binary univariate logistic regression analysis was used to assess independent predictors of IQ. Interrater agreement was assessed using Intraclass Correlation Coefficient (ICC). Results ‘DWI geometric distortion’ was significantly more pronounced in the HBB+/ME−/DR− (R1, 3.6 and R2, 4.0) as compared to the HBB−/ME+/DR− (R1, 4.2 and R2, 4.6) and HBB+/ME+/DR− (R1, 4.3 and R2, 4.7) cohort, respectively. Parameters ‘DWI IQ’ and ‘Whole MRI IQ’ were rated similarly by both readers. ME was a significant independent predictor of ‘good IQ’ for the whole MRI for R1 [b = 1.09, OR 2.98 (95% CI 1.29, 6.87)] and R2 [b = 1.01, OR 2.73 (95% CI 1.24, 6.04)], respectively. Data conclusion ME seems to significantly improve image quality of DWI and the whole mpMRI image set of the prostate. HBB and DR did not have any benefit.

scholarly journals POS0842 LUNG ULTRASOUND TO ASSESS THE SEVERITY OF INTERSTITIAL LUNG DISEASE IN SYSTEMIC SCLEROSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 675.1-675
Author(s):  
C. Bruni ◽  
L. Mattolini ◽  
L. Tofani ◽  
L. Gargani ◽  
N. Landini ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Quantitative Methods ◽  
Lung Ultrasound ◽  
Significant Negative Correlation ◽  
High Resolution Computed Tomography ◽  
Quantitative Score ◽  
Quantitative Scoring ◽  
B Lines

Background:Interstitial lung disease (ILD) is one of the most common complications and one of the main causes of morbidity and mortality in Systemic Sclerosis (SSc). High-resolution computed tomography (HRCT) is the gold standard for the diagnosis of ILD and it allows its quantification. Among semi-quantitative methods, Goh et al proposed a semi-quantitative scoring system to visually quantify ILD extent, with categorical cut-off of 20% to distinguish limited and extensive parenchymal involvement with prognostic implications. More recently, the use of radiomics has allowed the objective quantification of ILD through the use of dedicated software, which calculate different parameters of lung density.Given the exposure to ionizing radiation that the procedure entails, other methods of ILD evaluation are being studied, among which lung ultrasound (LUS) identifies the B-lines as a main feature of ILD. So far, different evidences have proposed the use of LUS for the screening of ILD, even in the early phases of the disease and in subclinical lung involvement.Objectives:the aim of this study is to test the role of LUS in quantifying the severity of SSc-ILD, evaluated with both semi-quantitative visual radiological and quantitative radiomic scores.Methods:Adult SSc patients classified according to the ACR/EULAR 2013 criteria patients were assessed with pulmonary function test (PFTs), lung ultrasound and HRCT over 60 days. CT images were analysed qualitatively (by presence/absence of ILD), semi-quantitatively (categorical Goh score <20% vs> 20% of extent and the continuous extent Goh score made from 5 levels’ assessment– 0 to 100%) and quantitatively [with the densitometric radiomic data obtained through the Horos software - Mean lung attenuation (MLA), Standard Deviation (SD), Kurtosis, Skewness and Lung volume (LV)]. LUS was used to quantify the B-lines detected in each patient by scanning a total of 13 intercostal spaces, on both anterior and posterior chest wall.Results:Among 59 SSc patients (81% women, mean age 48±14 years, 45% anti-Scl70 positive), 23 (39%) presented ILD on HRCT, of which 14 limited and 9 extensive. The mean visual semi-quantitative score was 6%, ranging from 0 to 66%. Our data showed a significantly different number of B-Lines in ILD vs non-ILD patients (median 38 vs 9, p <.005), a result which was further confirmed among non-ILD vs ILD> 20% (median 47 vs 9, p=.001) and ILD <20% (median 36 vs 9, p=.001) patients. Conversely, the number of B-lines was not statistically different between patients with ILD <20% and >20% (median 47 vs 36, p=.78). We observed a significant negative correlation between the number of B-lines and FVC (r=-.472, p<.05) TLC (r=-.436, p=.003), DLco (r=-.515, p<.001), DLCO/VA (r=.-306, p=.03). Finally, the number of B-lines showed a statistically significant correlation with the Goh score on 5 levels (r=.437, p=.001), MLA (r=.571, p<.001), kurtosis (r=-.285, p=.028), skewness (r=-.370, p = .004) and LV (r=-.277, p=.033). All data were confirmed analysing anterior and posterior B-Lines separately.Conclusion:Our study confirms that LUS represents a useful tool for the identification of SSc-ILD. In addition, we showed that LUS may be useful also for the quantification of the severity of SSc-ILD, by correlating with PFT parameters, radiomics parameters and visual radiological evaluation. Together with the PFTs, LUS could be used to increase the accuracy of the screening and, potentially, of the follow-up of SSc-ILD patients.Disclosure of Interests:Cosimo Bruni: None declared, Lavinia Mattolini: None declared, Lorenzo Tofani: None declared, Luna Gargani Consultant of: GE Healthcare, Philips Healthcare and Caption Health, Nicholas Landini: None declared, Gemma Lepri: None declared, Martina Orlandi: None declared, Serena Guiducci: None declared, Silvia Bellando Randone: None declared, Marco Matucci-Cerinic: None declared

scholarly journals Patients in intensive care unit for COVID-19 pneumonia: the lung ultrasound patterns at admission and discharge. An observational pilot study

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Persona Paolo ◽  
Valeri Ilaria ◽  
Zarantonello Francesco ◽  
Forin Edoardo ◽  
Sella Nicolò ◽  
...  
Keyword(s):  
Intensive Care ◽  
Lung Ultrasound ◽  
Low Cost ◽  
Low Frequency ◽  
Specific Pattern ◽  
University Hospital ◽  
Left Hemithorax ◽  
Significant Difference ◽  
The Right ◽  
B Lines

Abstract Background During COVID-19 pandemic, optimization of the diagnostic resources is essential. Lung Ultrasound (LUS) is a rapid, easy-to-perform, low cost tool which allows bedside investigation of patients with COVID-19 pneumonia. We aimed to investigate the typical ultrasound patterns of COVID-19 pneumonia and their evolution at different stages of the disease. Methods We performed LUS in twenty-eight consecutive COVID-19 patients at both admission to and discharge from one of the Padua University Hospital Intensive Care Units (ICU). LUS was performed using a low frequency probe on six different areas per each hemithorax. A specific pattern for each area was assigned, depending on the prevalence of A-lines (A), non-coalescent B-lines (B1), coalescent B-lines (B2), consolidations (C). A LUS score (LUSS) was calculated after assigning to each area a defined pattern. Results Out of 28 patients, 18 survived, were stabilized and then referred to other units. The prevalence of C pattern was 58.9% on admission and 61.3% at discharge. Type B2 (19.3%) and B1 (6.5%) patterns were found in 25.8% of the videos recorded on admission and 27.1% (17.3% B2; 9.8% B1) on discharge. The A pattern was prevalent in the anterosuperior regions and was present in 15.2% of videos on admission and 11.6% at discharge. The median LUSS on admission was 27.5 [21–32.25], while on discharge was 31 [17.5–32.75] and 30.5 [27–32.75] in respectively survived and non-survived patients. On admission the median LUSS was equally distributed on the right hemithorax (13; 10.75–16) and the left hemithorax (15; 10.75–17). Conclusions LUS collected in COVID-19 patients with acute respiratory failure at ICU admission and discharge appears to be characterized by predominantly lateral and posterior non-translobar C pattern and B2 pattern. The calculated LUSS remained elevated at discharge without significant difference from admission in both groups of survived and non-survived patients.

scholarly journals 19 Automated Quantification Of B-Lines in Lung Ultrasound On COVID-19 Patients

2021 ◽  
Vol 78 (2) ◽  
pp. S9-S10
Author(s):  
C. Baloescu ◽  
A. Chen ◽  
B. Raju ◽  
N. Evans ◽  
C.L. Moore
Keyword(s):  
Lung Ultrasound ◽  
Automated Quantification ◽  
B Lines

scholarly journals Lung Ultrasound Patterns and Clinical-Laboratory Correlates during COVID-19 Pneumonia: A Retrospective Study from North East Italy

2021 ◽  
Vol 10 (6) ◽  
pp. 1288
Author(s):  
Riccardo Senter ◽  
Federico Capone ◽  
Stefano Pasqualin ◽  
Lorenzo Cerruti ◽  
Leonardo Molinari ◽  
...  
Keyword(s):  
Lung Ultrasound ◽  
Clinical Laboratory ◽  
Imaging Modality ◽  
Case Series ◽  
Severity Index ◽  
Disease Stage ◽  
Retrospective Case ◽  
North East ◽  
High Flow Oxygen ◽  
B Lines

Background and Aim. Lung ultrasound (LUS) is a convenient imaging modality in the setting of coronavirus disease-19 (COVID-19) because it is easily available, can be performed bedside and repeated over time. We herein examined LUS patterns in relation to disease severity and disease stage among patients with COVID-19 pneumonia. Methods. We performed a retrospective case series analysis of patients with confirmed SARS-CoV-2 infection who were admitted to the hospital because of pneumonia. We recorded history, clinical parameters and medications. LUS was performed and scored in a standardized fashion by experienced operators, with evaluation of up to 12 lung fields, reporting especially on B-lines and consolidations. Results. We included 96 patients, 58.3% men, with a mean age of 65.9 years. Patients with a high-risk quick COVID-19 severity index (qCSI) were older and had worse outcomes, especially for the need for high-flow oxygen. B-lines and consolidations were located mainly in the lower posterior lung fields. LUS patterns for B-lines and consolidations were significantly worse in all lung fields among patients with high versus low qCSI. B-lines and consolidations were worse in the intermediate disease stage, from day 7 to 13 after onset of symptoms. While consolidations correlated more with inflammatory biomarkers, B-lines correlated more with end-organ damage, including extrapulmonary involvement. Conclusions. LUS patterns provide a comprehensive evaluation of patients with COVID-19 pneumonia that correlated with severity and dynamically reflect disease stage. LUS patterns may reflect different pathophysiological processes related to inflammation or tissue damage; consolidations may represent a more specific sign of localized disease, whereas B-lines seem to be also dependent upon generalized illness due to SARS-CoV-2 infection.

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