Key result
A backpropagation algorithm-based color Doppler ultrasound imaging algorithm achieved a higher Dice similarity coefficient (96.44%) compared to APES (83.97%) and LCA (84.74%) algorithms (P<0.05).
Why the study?
This study was conducted to analyze related risk factors and perinatal outcomes of pregnant women with gestational diabetes mellitus based on color Doppler ultrasound diagnosis.
Does a Backpropagation algorithm-based color Doppler ultrasound imaging algorithm improve diagnostic accuracy and reduce calculation time in pregnant women with gestational diabetes mellitus compared to APES and LCA algorithms?
Observational (n=80)
Does a Backpropagation algorithm-based color Doppler ultrasound imaging algorithm improve diagnostic accuracy and reduce calculation time in pregnant women with gestational diabetes mellitus compared to APES and LCA algorithms?
A backpropagation neural network algorithm improves the diagnostic accuracy and processing time of color Doppler ultrasound in evaluating fetuses of pregnant women with gestational diabetes mellitus.
May enhance fetal ultrasound analysis in GDM; leaves open prospective clinical validation before practice change.
In this work, the related risk factors and perinatal outcomes of pregnant women with gestational diabetes mellitus (GDM) were analyzed based on color Doppler ultrasound (CDU) diagnosis. Backpropagation (BP) algorithm-based CDU imaging algorithm (BPC) was constructed in this study and applied in CDU images of 80 pregnant women with GDM. Besides, amplitude and phase estimation (APES) and low-complexity adaptive beam (LCA) algorithms were introduced for comparison with BPC in turn. It was found that Dice similarity coefficient (96.44%), sensitivity (95.45%), and specificity (91.56%) of BPC were greater than those of APES (83.97%, 85.84%, 78.45%) and LCA (84.74%, 86.29%, 82.35%), while its running time (6.44 ± 1.39 s) was shorter than that of APES (11.87 ± 2.41 s) and LCA (13.76 ± 1.54 s) ( P < 0.05 ). Pregnant women in the experimental group (group B) were older than those in the control group (group A) ( P < 0.05 ). The pulsatility index (PI) and renal artery resistance index (RI) of fetuses in group B (0.95 ± 0.15) were higher than those of group A (0.57 ± 0.24) ( P < 0.05 ). In addition, pregnancy age, family history of hypertension, and abortion history were positively correlated with GDM ( P < 0.05 ). In conclusion, BPC could not only improve diagnosis accuracy in fetuses’ CDU images but also shorten calculation time. Pregnancy age, family history of hypertension, and abortion history were the related risk factors for GDM in pregnant women.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2021) conducted an observational in Gestational diabetes mellitus (n=80). Backpropagation algorithm-based CDU imaging algorithm (BPC) vs. APES and LCA algorithms was evaluated on Dice similarity coefficient (p=<0.05). A backpropagation algorithm-based color Doppler ultrasound imaging algorithm achieved a higher Dice similarity coefficient (96.44%) compared to APES (83.97%) and LCA (84.74%) algorithms (P<0.05).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: