Detection of pneumothorax on ultrasound using artificial intelligence

Montgomery, Sean P.; Li, Forrest; Funk, Christopher; Peethumangsin, Erica; Morris, Michael H.; Anderson, Jess; Hersh, Andrew; Aylward, Stephen

doi:10.1097/ta.0000000000003845

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…In this case, the curtain sign is lost and the mobile atelectatic lung appears as a tissue dense echogenicity undulating within the fluid (jellyfish sign) 15 . Pneumothorax signs considered included loss of the normal lung sliding and presence of a lung point, defined as the point where the sliding lung can be seen together with the nonsliding pleura in the same image, and the sliding lung is observed to encroach on the still part and recede again with breathing 21,22 . The following findings of pulmonary fibrosis were included 23,24 ; (1) thickening (>3 mm) of the pleural line, (2) irregularity of the pleural line (i.e., loss of the normal linear contour, aside from the normal or thickened appearance of the pleura), (3) increased number of vertical artifacts (>3 between two ribs in a single US scan), (4) subpleural nodulations.…”

Section: Methodsmentioning

confidence: 99%

Breast ultrasound: An opportunity to detect unsuspected pleural and pulmonary abnormalities

Corvino,

Basile,

Boccatonda

et al. 2024

J of Clinical Ultrasound

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PurposeWhile scanning women for breast US, is possible to observe changes in the appearance of the pleural line or in the most superficial portion of the lung. The objective of this single‐center, prospective study was to determine the prevalence of a variety of pleural and pulmonary US findings during routine breast US.MethodsIn this study, there were 200 women undergoing standard breast US examination. The presence of pleural and pulmonary abnormalities in these cases was recorded. Two off‐site reviewers confirmed the presence of pleura and lung changes.ResultsThere was no abnormal finding in 168 out of 200 cases (84%) while there were one or more abnormal findings in 32 cases (16%). Pleural effusion was observed in 0.5% of cases, thickening of the pleural line 5% of cases, irregularity of the pleural line in 6% of cases, increased number of vertical artifacts in 9% of cases, subpleural nodulations in 2% of cases, and lung consolidation in 0.5%.ConclusionPleural and lung changes are not uncommon during breast US. Operators performing breast US examinations should be aware of the possibility to identify unsuspected pleuro‐pulmonary abnormalities.

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Section: Methodsmentioning

confidence: 99%

Breast ultrasound: An opportunity to detect unsuspected pleural and pulmonary abnormalities

Corvino,

Basile,

Boccatonda

et al. 2024

J of Clinical Ultrasound

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“…To assist novice operators and alleviate the workloads of the experts, objective information obtained from the quantitative analysis of the LUS M-mode image is useful and helpful. Deep learning-based methods are powerful tools for detection of lung sliding and characterization of pneumothorax ( 16 , 17 ). However, requirement of large training data with labels, inconsistent system settings, scanning protocols, and annotations, and most importantly, unclear underlaying mechanisms limited their use in clinical.…”

Section: Introductionmentioning

confidence: 99%

Quantitative assessment of pneumothorax by using Shannon entropy of lung ultrasound M-mode image and diaphragmatic excursion based on automated measurement

Fang,

Shen,

Ting

et al. 2024

Quant Imaging Med Surg

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Background Lung ultrasound (LUS) and diaphragm ultrasound (DUS) are the appropriate modalities for conservative observation to those patients who are with stable pneumothorax, as well as for the timely detection of life-threatening pneumothorax at any location, due to they are portable, real-time, relatively cost effective, and most important, without radiation exposure. The absence of lung sliding on LUS M-mode images and the abnormality of diaphragmatic excursion (DE) on DUS M-mode images are the most common and novel diagnostic criteria for pneumothorax, respectively. However, visual inspection of M-mode images remains subjective and quantitative analysis of LUS and DUS M-mode images are required. Methods Shannon entropy of LUS M-mode image (ShanEn LM ) and DE based on the automated measurement (DE AM ) are adapted to the objective pneumothorax diagnoses and the severity quantifications in this study. Mild, moderate, and severe pneumothoraces were induced in 24 male New Zealand rabbits through insufflation of room air (5, 10 and 15, and 25 and 40 mL/kg, respectively) into their pleural cavities. In vivo intercostal LUS and subcostal DUS M-mode images were acquired using a point-of-care system for estimating ShanEn LM and DE AM . Results ShanEn LM and DE AM as functions of air insufflation volumes exhibited U-shaped curves and were exponentially decreasing, respectively. Either ShanEn LM or DE AM had areas under the receiver operating characteristic curves [95% confidence interval (CI)] of 1.0000 (95% CI: 1.0000–1.0000), 0.9833 (95% CI: 0.9214–1.0000), and 0.9407 (95% CI: 0.8511–1.0000) for differentiating between normal and mild pneumothorax, mild and moderate pneumothoraces, and moderate and severe pneumothoraces, respectively. Conclusions Our findings imply that the combination of ShanEn LM and DE AM give the promising potential for pneumothorax quantitative diagnosis.

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“…Smart medicine applications have been extensively reviewed elsewhere [25][26][27][28][29][30]. Focusing on AI for interpreting ultrasound images [31], applications include the identification of tumors [32], diagnosing infectious disease [33,34], and determining eFAST scan outcomes [35,36], among others. Each of these applications often relies on deep convolutional neural networks, which extract image features and parameter weights to identify differences in images.…”

Section: Introductionmentioning

confidence: 99%

Toward Smart, Automated Junctional Tourniquets—AI Models to Interpret Vessel Occlusion at Physiological Pressure Points

Avital,

Hernandez Torres,

Knowlton

et al. 2024

Bioengineering

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Hemorrhage is the leading cause of preventable death in both civilian and military medicine. Junctional hemorrhages are especially difficult to manage since traditional tourniquet placement is often not possible. Ultrasound can be used to visualize and guide the caretaker to apply pressure at physiological pressure points to stop hemorrhage. However, this process is technically challenging, requiring the vessel to be properly positioned over rigid boney surfaces and applying sufficient pressure to maintain proper occlusion. As a first step toward automating this life-saving intervention, we demonstrate an artificial intelligence algorithm that classifies a vessel as patent or occluded, which can guide a user to apply the appropriate pressure required to stop flow. Neural network models were trained using images captured from a custom tissue-mimicking phantom and an ex vivo swine model of the inguinal region, as pressure was applied using an ultrasound probe with and without color Doppler overlays. Using these images, we developed an image classification algorithm suitable for the determination of patency or occlusion in an ultrasound image containing color Doppler overlay. Separate AI models for both test platforms were able to accurately detect occlusion status in test-image sets to more than 93% accuracy. In conclusion, this methodology can be utilized for guiding and monitoring proper vessel occlusion, which, when combined with automated actuation and other AI models, can allow for automated junctional tourniquet application.

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Detection of pneumothorax on ultrasound using artificial intelligence

Abstract: We developed an artificial intelligence system to identify ribs and pleura and determine if a pneumothorax is present on point of care ultrasound.

Cited by 6 publications

References 11 publications

Breast ultrasound: An opportunity to detect unsuspected pleural and pulmonary abnormalities

Breast ultrasound: An opportunity to detect unsuspected pleural and pulmonary abnormalities

Quantitative assessment of pneumothorax by using Shannon entropy of lung ultrasound M-mode image and diaphragmatic excursion based on automated measurement

Toward Smart, Automated Junctional Tourniquets—AI Models to Interpret Vessel Occlusion at Physiological Pressure Points

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