Potential of Artificial Intelligence for Estimating Japanese Fetal Weights

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We developed an artificial intelligence (AI) method for estimating fetal weights of Japanese fetuses based on the gestational weeks and the bi-parietal diameter, abdominal circumference, and femur length. The AI comprised of neural network architecture was trained by deep learning with a dataset that consists of ± 2 standard devia-tion (SD), ± 1.5SD, and ± 0SD categories of the approved standard values of ultrasonic measurements of the fetal weights of Japanese fetuses (Japan Society of Ultrasonics in Medicine [JSUM] data). We investigated the residuals and compared 2 other regression formulae for estimating the fetal weights of Japanese fetuses by t-test and Bland-Altman analyses, respectively. The residuals of the AI for the test dataset that was 12.5% of the JSUM data were 6.4 ± 2.6, −3.8 ± 8.6, and −0.32 ± 6.3 (g) at −2SD, +2SD, and all categories, respectively. The residu-als of another AI method created with all of the JSUM data, of which 20% were randomized validation data, were −1.5 ± 9.4, −2.5 ± 7.3, and −1.1 ± 6.7 (g) for −2SD, +2SD, and all categories, respectively. The residuals of this AI were not different from zero, whereas those of the published formulae differed from zero. Though vali-dation is required, the AI demonstrated potential for generating fetal weights accurately, especially for extreme fetal weights.

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  • Acta Medica Okayama

    Acta Medica Okayama 74 (6), 483-493, 2020-12

    Okayama University Medical School

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