Acta Medica Okayama volume74 issue6
2020-12 発行

Potential of Artificial Intelligence for Estimating Japanese Fetal Weights

Miyagi, Yasunari Medical Data Labo
Miyake, Takahito Department of Obstetrics and Gynecology, Miyake Clinic
Publication Date
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.
Document Type
Original Article
deep learning
artificial intelligence
fetal weight
neural network
ultrasound biometry
Link to PubMed
74_6_483.pdf 3.57 MB