Machine Learning Algorithm Detection of Confluent B-Lines

B-lines are a ring-down artifact of lung ultrasound that arise with increased alveolar water in conditions such as pulmonary edema and infectious pneumonitis. Confluent B-line presence may signify a different level of pathology compared with single B-lines. Existing algorithms aimed at B-line counti...

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Vydané v:Ultrasound in medicine & biology Ročník 49; číslo 9; s. 2095
Hlavní autori: Baloescu, Cristiana, Rucki, Agnieszka A, Chen, Alvin, Zahiri, Mohsen, Ghoshal, Goutam, Wang, Jing, Chew, Rita, Kessler, David, Chan, Daniela K I, Hicks, Bryson, Schnittke, Nikolai, Shupp, Jeffrey, Gregory, Kenton, Raju, Balasundar, Moore, Christopher
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England 01.09.2023
Predmet:
Adult
Algorithms
Humans
Lung - diagnostic imaging
Machine Learning
Prospective Studies
Pulmonary Edema
Ultrasonography - methods
Point-of-care ultrasound
B-line
Lung ultrasound
Artificial intelligence
Machine learning
ISSN:1879-291X, 1879-291X
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Popis
Shrnutí:B-lines are a ring-down artifact of lung ultrasound that arise with increased alveolar water in conditions such as pulmonary edema and infectious pneumonitis. Confluent B-line presence may signify a different level of pathology compared with single B-lines. Existing algorithms aimed at B-line counting do not distinguish between single and confluent B-lines. The objective of this study was to test a machine learning algorithm for confluent B-line identification. This study used a subset of 416 clips from 157 subjects, previously acquired in a prospective study enrolling adults with shortness of breath at two academic medical centers, using a hand-held tablet and a 14-zone protocol. After exclusions, random sampling generated a total of 416 clips (146 curvilinear, 150 sector and 120 linear) for review. A group of five experts in point-of-care ultrasound blindly evaluated the clips for presence/absence of confluent B-lines. Ground truth was defined as majority agreement among the experts and used for comparison with the algorithm. Confluent B-lines were present in 206 of 416 clips (49.5%). Sensitivity and specificity of confluent B-line detection by algorithm compared with expert determination were 83% (95% confidence interval [CI]: 0.77-0.88) and 92% (95% CI: 0.88-0.96). Sensitivity and specificity did not statistically differ between transducers. Agreement between algorithm and expert for confluent B-lines measured by unweighted κ was 0.75 (95% CI: 0.69-0.81) for the overall set. The confluent B-line detection algorithm had high sensitivity and specificity for detection of confluent B-lines in lung ultrasound point-of-care clips, compared with expert determination.
Bibliografia:ObjectType-Article-1
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content type line 23
ISSN:1879-291X
1879-291X
DOI:10.1016/j.ultrasmedbio.2023.05.016