OBJECTIVES: This study evaluated the role of ultrasound in postoperative care after major lung resection. BACKGROUND: High accuracy of lung ultrasound imaging was proved in various medical fi elds. The experience with ultrasound after thoracic surgery is limited. METHODS: Patients scheduled for major lung resection were consecutively included in a prospective study comparing two modalities of imaging examinations, namely those employing ultrasound and X-ray in the diagnoses of pneumothorax and pleural effusion. Two examinations were performed. One after recovery from anaesthesia, the second before chest tube removal. RESULTS: Forty-eight patients underwent 87 examinations. X-ray and ultrasound examinations showed substantial and fair agreements for pneumothorax (Cohen's kappa coeffi cients 0.775 and 0.397) and slight and substantial agreements for pleural effusion (Cohen's kappa coeffi cients 0.036 and 0.611). The sensitivity bounds for pneumothorax were 45.5-58.5 % at the fi rst and 29.7-59.4 % at the second examination. Sensitivity bounds for pleural effusion were 0-86.2 % at the fi rst and 32.6-36.9 % at the second examination. Except for two cases of pneumothorax being missed by X-ray imaging, the rest of mismatches were clinically irrelevant conditions with no impact on clinical decision and patient's outcome. CONCLUSION: The use of ultrasound can reduce the number of X-ray examinations and thus lower the radiation exposure after major lung resections (Tab. 4, Ref. 30).
Certain post-thoracic surgery complications are monitored in a standard manner using methods that employ ionising radiation. A need to automatise the diagnostic procedure has now arisen following the clinical trial of a novel lung ultrasound examination procedure that can replace X-rays. Deep learning was used as a powerful tool for lung ultrasound analysis. We present a novel deep-learning method, automated M-mode classification, to detect the absence of lung sliding motion in lung ultrasound. Automated M-mode classification leverages semantic segmentation to select 2D slices across the temporal dimension of the video recording. These 2D slices are the input for a convolutional neural network, and the output of the neural network indicates the presence or absence of lung sliding in the given time slot. We aggregate the partial predictions over the entire video recording to determine whether the subject has developed post-surgery complications. With a 64-frame version of this architecture, we detected lung sliding on average with a balanced accuracy of 89%, sensitivity of 82%, and specificity of 92%. Automated M-mode classification is suitable for lung sliding detection from clinical lung ultrasound videos. Furthermore, in lung ultrasound videos, we recommend using time windows between 0.53 and 2.13 s for the classification of lung sliding motion followed by aggregation.
Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods to detect two types of lung artifacts, namely A- and B-lines. We evaluate the proposed approach on the POCUS dataset and data acquired from a hospital. We show that by using the Fourier transform, we can analyze lung ultrasound images in real-time and classify videos with an accuracy above 70%. We also evaluate the method’s applicability for segmentation, showcasing its high success rate for B-lines (89% accuracy) and its shortcomings for A-line detection. We then propose a hybrid solution that uses a combination of neural networks and analytical methods to increase accuracy in horizontal line detection, emphasizing the pleura.
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