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Abstract
The aim was to analyze the application values and diagnostic effects of transvaginal 3-dimensional (3D) ultrasonic image based on extreme learning machine denoising algorithm (ELMDA) in the diagnosis of intrauterine adhesions (IUA). The speckle noise in the 3D ultrasound image was removed with the ELMDA. Its peak signal-to-noise ratio (PSNR) and the mean square error (MSE) were compared with those of the median filter algorithm (MFA) with the anisotropic diffusion algorithm (ADA) and wavelet threshold. The ELMDA was used in the diagnosis of 3D ultrasound images to compare the accuracy of hysteroscopy with transvaginal 3D ultrasound and two-dimensional (2D) ultrasound in the diagnosis of IUA. The results showed that the MSE of ELMDA was dramatically smaller than those of ADA and WT-MFA and its PSNR was higher than those of the other two algorithms ( P < 0.05) when the noise variance was constant. The diagnostic accuracy of mild and moderate adhesions by 2D ultrasound was statistically different ( P < 0.05) compared with hysteroscopy. But the diagnosis results of severe adhesions were consistent, and the diagnosed cases were both 6 (11.11%) with no statistical difference ( P > 0.05). In addition, there was no statistically great difference in the diagnostic accuracy of IUA by transvaginal 3D ultrasound and hysteroscopy ( P > 0.05), and the diagnosis results of moderate and severe adhesions were consistent (both 20 cases (37.04%) and 6 cases (11.11%), respectively) with no statistical difference ( P > 0.05). The diagnostic accuracy of 3D ultrasound was 96.30%, while that of 2D ultrasound was 90.74%, showing a statistical difference ( P < 0.05). In conclusion, ELMDA had a good effect of denoising, and there was a high accuracy of the application of 3D transvaginal ultrasound to diagnose IUA, which had reliable clinical application value.
Highlights
intrauterine adhesions (IUA) is known as Asherman’s syndrome [1]
54 patients with IUA were examined by hysteroscopy and all of them were diagnosed with IUA: 28 patients with mild adhesions (51.85%), 20 patients with moderate adhesions (37.04%), and 6 patients with severe adhesions (11.11%) (Figure 4)
extreme learning machine denoising algorithm (ELMDA) was applied to 3D ultrasound examination to diagnose IUA and its results were compared with those of hysteroscopy and transvaginal 2D ultrasound examination. e final results revealed that mean square error (MSE) of ELMDA was dramatically smaller than those of anisotropic diffusion algorithm (ADA) and WT-median filter algorithm (MFA), and the difference was statistically obvious (P < 0.05)
Summary
IUA is known as Asherman’s syndrome [1]. IUA is one of the common gynecological diseases with a high clinical incidence. is is because multiple factors result in damage of the endometrial basement layer to cause the adhesions of cervical canal or uterine muscle for partial or total occlusion of the uterine cavity, thereby emerging with menstrual abnormalities, infertility, and repeated abortion in patients [2, 3]. The incidence of IUA has kept rising with the increase of abortion rate, which has become the second major cause of secondary infertility after fallopian tube factors, posing a serious threat to women’s health [4, 5]. Hysteroscopy is currently an effective operation for the treatment of IUA and the gold standard for diagnosis [6]. E commonly used hysteroscopy and hysterography are both invasive examinations, and transvaginal 2D ultrasound examination has certain limitations so that the diagnostic accuracy of IUA is low [7, 8]. With the continuous development of ultrasound technology and the improvement of the instrument resolution, 3D transvaginal sonograph (3D-TVS) has made up for the defects of 2D ultrasound and has become a new method for clinical diagnosis of IUA [9]. 3D ultrasonic images are directly reconstructed based on the 2D ultrasonic images
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