Novel foetal echocardiographic image processing software (5D Heart) improves the display of key diagnostic elements in foetal echocardiography

To evaluate the clinical value of foetal intelligent navigation echocardiography (5D Heart) for the display of key diagnostic elements in basic sections. 3D volume datasets of 182 normal singleton foetuses were acquired with a four chamber view by using a volume probe. After processing the datasets...

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Published in:BMC medical imaging Vol. 20; no. 1; p. 33
Main Authors: Hu, Wan-Yu, Zhou, Jin-Hong, Tao, Xiao-Ying, Li, Shi-Yan, Wang, Bei, Zhao, Bo-Wen
Format: Journal Article
Language:English
Published: England BioMed Central Ltd 03-04-2020
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Summary:To evaluate the clinical value of foetal intelligent navigation echocardiography (5D Heart) for the display of key diagnostic elements in basic sections. 3D volume datasets of 182 normal singleton foetuses were acquired with a four chamber view by using a volume probe. After processing the datasets by using 5D Heart, eight cardiac diagnostic planes were demonstrated, and the image qualities of the key diagnostic elements were graded by 3 doctors with different experiences in performing foetal echocardiography. A total of 231 volume datasets acquired from the 182 normal foetuses were used for 5D Heart analysis and display. The success rate of 8 standard diagnostic views was 88.2%, and the success rate of each diagnostic view was 55.8-99.2% and 70.7-99.0% for the random four chamber view as the initial section and for the apical four chamber view as the initial section, respectively. The success rate of each diagnostic element in the 8 diagnostic sections obtained by 5D Heart was 58.9%~ 100%. Excellent agreement was found between experienced sonographers and less-experienced sonographers (kappa> 0.769). Inter- and intra-observer agreement were substantial to near-perfect, kappa values ranging from 0.612 to 1.000 (Cohen's kappa). 5D Heart can significantly improve the image quality of key diagnostic elements in foetal echocardiography with low operator dependency and good reproducibility.
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ISSN:1471-2342
1471-2342
DOI:10.1186/s12880-020-00429-8