Transvaginal three-dimensional ultrasound assessment of embryonic genital tubercle at 8-10+6 weeks of gestation.

OBJECTIVES: To assess embryonic genital tubercle using transvaginal three-dimensional (3D) ultrasound at 8-10+6 weeks of gestation. METHODS: One-hundred and two transvaginal 3D ultrasound scans were performed for first-trimester dating at 8-10+6 weeks of gestation. The genital tubercle angle (GTA) and genital tubercle length (GTL) were measured with a mid-sagittal view of the embryo using the 3D ultrasound multiplanar mode. Intra- and inter-observer agreements regarding GTA and GTL were also assessed with Bland-Altman plots and intra- and inter-correlation coefficients. RESULTS: There were no significant differences in GTA between male and female embryos at 8, 9, 10 weeks, or 8-10+6 weeks of gestation, respectively. There were also no significant differences in GTL between male and female embryos at 8, 9, 10 weeks, or 8-10+6 weeks of gestation, respectively. However, GTL increased linearly with advancing gestation (r=0.8276, p<0.00001). Mean GTL (SD) values at 8, 9, and 10 weeks were 0.833 mm (0.274), 1.623 mm (0.262), and 2.152 mm (0.420), respectively (p<0.001). Intra- and inter-reproducibilities of GTA and GTL were excellent. The intra- and inter-correlation coefficients of GTA and GTL were 0.964 and 0.995, and 0.996 and 0.9933, respectively. CONCLUSIONS: The genital tubercle could be identified using transvaginal 3D ultrasound at 8-10+6 weeks of gestation. However, sex differentiation could not be performed at this age. The genital tubercle linearly developed with advancing gestation during the mid-first trimester of pregnancy.

A Novel Method for Determining Fibrin/Fibrinogen Degradation Products and Fibrinogen Threshold Criteria via Artificial Intelligence in Massive Hemorrhage during Delivery with Hematuria.

(1) Background: Although the diagnostic criteria for massive hemorrhage with organ dysfunction, such as disseminated intravascular coagulation associated with delivery, have been empirically established based on clinical findings, strict logic has yet to be used to establish numerical criteria. (2) Methods: A dataset of 107 deliveries with >2000 mL of blood loss, among 13,368 deliveries, was obtained from nine national perinatal centers in Japan between 2020 and 2023. Twenty-three patients had fibrinogen levels <170 mg/dL, which is the initiation of coagulation system failure, according to our previous reports. Three of these patients had hematuria. We used six machine learning methods to identify the borderline criteria dividing the fibrinogen/fibrin/fibrinogen degradation product (FDP) planes, using 15 coagulation fibrinolytic factors. (3) Results: The boundaries of hematuria development on a two-dimensional plane of fibrinogen and FDP were obtained. A positive FDP-fibrinogen/3-60 (mg/dL) value indicates hematuria; otherwise, the case is nonhematuria, as demonstrated by the support vector machine method that seemed the most appropriate. (4) Conclusions: Using artificial intelligence, the borderline criterion was obtained, which divides the fibrinogen/FDP plane for patients with hematuria that could be considered organ dysfunction in massive hemorrhage during delivery; this method appears to be useful.

Color Doppler, HDlive, and HDlive Silhouette features of an umbilical cord cyst before 11 weeks of gestation: Diagnostic value.

OBJECTIVE: We describe transvaginal color Doppler, HDlive, and HDlive Silhouette features of an umbilical cord cyst (UCC) before 11 weeks of gestation. METHODS: In this cohort study, 135 transvaginal dating scans were performed at 7 to 10 + 6 weeks of gestation, and 17 UCCs were identified (12.6%). UCC was evaluated using color Doppler, HDlive, and HDlive Silhouette. The clinical characteristics, pregnancy courses, and outcomes were also investigated. RESULTS: UCC location was on the fetal side in six cases, at the free loop in 10 cases, and on the placental side in one case. There were seven single and 10 multiple cysts. Cyst diameters ranged from 3.3 to 11.3 mm (mean, 5.6; standard deviation, ±2.1). Blood flow inside the cyst was noted in three cases (17.6%). HDlive clearly showed the spatial relationships among UCC, the umbilical cord, midgut herniation, yolk sac, and embryo. Location of UCC could be clearly identified with HDlive. HDlive Silhouette showed central cysts inside UCCs in seven cases (41.2%). HDlive Silhouette also clearly demonstrated the sac of midgut herniation in the umbilical cord in 12 cases (70.6%). All UCCs resolved before 15 weeks (mean, 11.1 weeks; standard deviation, ±1.5). All fetuses with UCCs showed good neonatal outcomes. CONCLUSION: The incidence of UCC was high compared with that in previous reports. Color Doppler, HDlive, and HDlive Silhouette may provide information on the nature and origin of UCCs before 11 weeks of gestation. UCC before 11 weeks of gestation may be a common, transient, and benign finding.

Fetal brain activity and the free energy principle.

OBJECTIVES: To study whether the free energy principle can explain fetal brain activity and the existence of fetal consciousness via a chaotic dimension derived using artificial intelligence. METHODS: In this observational study, we used a four-dimensional ultrasound technique obtained to collect images of fetal faces from pregnancies at 27-37 weeks of gestation, between February and December 2021. We developed an artificial intelligence classifier that recognizes fetal facial expressions, which are thought to relate to fetal brain activity. We then applied the classifier to video files of facial images to generate each expression category's probabilities. We calculated the chaotic dimensions from the probability lists, and we created and investigated the free energy principle's mathematical model that was assumed to be linked to the chaotic dimension. We used a Mann-Whitney test, linear regression test, and one-way analysis of variance for statistical analysis. RESULTS: The chaotic dimension revealed that the fetus had dense and sparse states of brain activity, which fluctuated at a statistically significant level. The chaotic dimension and free energy were larger in the sparse state than in the dense state. CONCLUSIONS: The fluctuating free energy suggests consciousness seemed to exist in the fetus after 27 weeks.

Artificial intelligence to understand fluctuation of fetal brain activity by recognizing facial expressions.

OBJECTIVE: To examine whether artificial intelligence can achieve discoveries regarding fetal brain activity. METHODS: In this observational study, the authors collected images of fetal faces using a four-dimensional ultrasound technique obtained from singleton pregnancies of outpatients in routine practice at 27 to 37 weeks of gestation between February 1 and December 31, 2021. The authors developed an artificial intelligence classifier to recognize seven facial expressions of fetuses, then applied it to video files of fetal facial images to generate the probabilities, as confidence scores, of each expression category. Discrete Fourier transform and chaotic analysis were used to investigate the scores. Mann-Whitney test, t test, variance test, and one-way analysis of variance were used for statistical analysis. RESULTS: Facial expression changes were observed in cycles averaging 66 to 73 s. The power spectrum showed that mouthing and neutral expressions were the most prevalent. There was a difference between categories for the spectrum (p = 0.004). Two different states--dense and sparse--of confidence scores were discovered. The correlation dimension was 1.19 ± 0.22 and 1.33 ± 0.27 for dense and sparse, respectively (p = 0.047). CONCLUSION: This method objectively and quantitatively demonstrated fetal brain activity and may provide insight into how the fetus spends its time in utero.

Fetal Cerebellar Growth Curves Based on Biomathematics in Normally Developing Japanese Fetuses and Fetuses with Trisomy 18.

We used biomathematics to describe and compare cerebellar growth in normally developing and trisomy 18 Japanese fetuses. This retrospective study included 407 singleton pregnancies with fetuses at 14-39 weeks of gestation and 33 fetuses with trisomy 18 at 17-35 weeks. We used ultrasonography to measure fetal transverse cerebellar diameter (TCD) and anteroposterior cerebellar diameter (APCD). We hypothesized that cerebellar growth is proportional to cerebellar length at any given time point. We determined the formula L(t) ≒Keat+r, where e is Napier's number, t is time, L is cerebellar length, and a, K, and r are constants. We then obtained regression functions for each TCD and APCD in all fetuses. The regression equations for TCD and APCD values in normal fetuses, expressed as exponential functions, were TCD(t)=27.85e0.02788t-28.62 (mm) (adjusted R2=0.997), and APCD(t)=324.29e0.00286t-322.62 (mm) (adjusted R2=0.995). These functions indicated that TCD and APCD grew at constant rates of 2.788%/week and 0.286%/week, respectively, throughout gestation. TCD (0.0153%/week) and APCD (0.000430%/week) grew more slowly in trisomy 18 fetuses. This study demonstrates the potential of biomathematics in clinical research and may aid in biological understanding of fetal cerebellar growth.

HDlive Flow Silhouette with spatiotemporal image correlation for assessment of fetal cardiac structures at 12 to 14 + 6 weeks of gestation.

OBJECTIVES: To assess fetal cardiac structures using HDlive Flow Silhouette with spatiotemporal image correlation (STIC) at 12 to 14 + 6 weeks of gestation, and verify the feasibility of obtaining five cardiac views in the late first and early second trimesters of pregnancy. The fetal cardiac shape and the aspect of the descending aorta were also evaluated. METHODS: Eighty normal fetuses at 12 to 14 + 6 weeks of gestation were studied using trans-abdominal HDlive Flow Silhouette with STIC to assess the feasibility of five fetal cardiac views (frontal, spatial three-vessel, panoramic, posterior, and right lateral views). Target structures in each cardiac view were evaluated. 'Good' was assigned when all structures were noted, 'Fair' when only one structure was missed, and 'Poor' when two and more structures could not be detected. Frequencies of an elongated heart and those of a tortuous descending aorta were counted. RESULTS: Forty-nine fetuses were effectively included in the analysis. Success rates of 'Good' and 'Fair' were significantly higher with spatial three-vessel (p<0.01) and panoramic views (p<0.05). Frequencies of "Elongated heart", "Elongated left ventricle", and "Spherical heart" were 12.2, 6.2, and 81.6%, respectively. Frequencies of "Tortuous descending aorta" and "Straight descending aorta" were 12.2 and 87.8%, respectively. CONCLUSIONS: The feasibility of obtaining fetal five cardiac views using HDlive Flow Silhouette with STIC is good, and this technique provides useful information for evaluating fetal cardiac structures in the late first and early second trimesters of pregnancy.

The Optimal Prepregnancy Body Mass Index for Lactation in Japanese Women with Neonatal Separation as Analyzed by a Differential Equation.

We used a differential equation to identify the biological relationship between the maternal prepregnancy body mass index (BMI) and lactation on postpartum day 4 in Japanese women with neonatal separation. This retro-spective observational study included 252 mothers (135 primiparas, 117 multiparas) whose singleton neonates were admitted to a neonatal ICU. We formulated hypotheses based on breast anatomy to analyze the relation-ship between the expressed milk obtained on postpartum day 4 and the maternal prepregnancy BMI with the following differential equation: y'(x) = k y(x)/x, where k is the constant, x is the prepregnancy BMI, and y is the expressed milk volume. The formula was then obtained as y(x) = axk, where a is the constant. The Akaike information criterion (AIC) was used to estimate the regression equation with the maximum likelihood for primiparas and multiparas. The best criteria for BMI determined by the AIC were 20.89 kg/m2 in primiparas and 20.19 kg/m2 in multiparas. These were the optimal BMI values for lactation, coinciding with the median prepregnancy BMI in the study population (20.78 kg/m2 in primiparas and 20.06 kg/m2 in multiparas). The formula based on biomathematics might help establish the biological relationship between prepregnancy BMI and breastmilk volume.

Recognition of facial expression of fetuses by artificial intelligence (AI).

OBJECTIVES: The development of the artificial intelligence (AI) classifier to recognize fetal facial expressions that are considered as being related to the brain development of fetuses as a retrospective, non-interventional pilot study. METHODS: Images of fetal faces with sonography obtained from outpatient pregnant women with a singleton fetus were enrolled in routine conventional practice from 19 to 38 weeks of gestation from January 1, 2020, to September 30, 2020, with completely de-identified data. The images were classified into seven categories, such as eye blinking, mouthing, face without any expression, scowling, smiling, tongue expulsion, and yawning. The category in which the number of fetuses was less than 10 was eliminated before preparation. Next, we created a deep learning AI classifier with the data. Statistical values such as accuracy for the test dataset and the AI confidence score profiles for each category per image for all data were obtained. RESULTS: The number of fetuses/images in the rated categories were 14/147, 23/302, 33/320, 8/55, and 10/72 for eye blinking, mouthing, face without any expression, scowling, and yawning, respectively. The accuracy of the AI fetal facial expression for the entire test data set was 0.985. The accuracy/sensitivity/specificity values were 0.996/0.993/1.000, 0.992/0.986/1.000, 0.985/1.000/0.979, 0.996/0.888/1.000, and 1.000/1.000/1.000 for the eye blinking, mouthing, face without any expression, scowling categories, and yawning, respectively. CONCLUSIONS: The AI classifier has the potential to objectively classify fetal facial expressions. AI can advance fetal brain development research using ultrasound.

Potential of Artificial Intelligence for Estimating Japanese Fetal Weights.

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.

Success rate of five fetal cardiac views using HDlive Flow with spatiotemporal image correlation at 18-21 and 28-31 weeks of gestation.

Objective To assess the success rates of five fetal cardiac views using HDlive Flow (Silhouette) with spatiotemporal image correlation (STIC) in the second and third trimesters of pregnancy, and to verify the feasibility of obtaining five cardiac views by volumes. Methods One hundred and eighteen normal fetuses at 18-21 and 28-31 weeks of gestation were studied using HDlive Flow (Silhouette) with STIC to assess the success rates of five fetal cardiac views (frontal, spatial three-vessel, panoramic, posterior, and right lateral views). Target structures in each cardiac view were evaluated. "Good" was assigned when all structures were noted, "Fair" when only one structure was missed, and "Poor" when two and more structures could not been detected. Results There were no significant differences in success rates of each cardiac view between 18-21 and 28-31 weeks of gestation. The rate of "Good" with a spatial three-vessel view was significantly higher than that with other cardiac views at 18-21 and 28-31 weeks, respectively (P < 0.05). Conclusion Five cardiac views using HDlive Flow (Silhouette) with STIC may become an adjunctive and useful tool in fetal cardiac examination.

Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types.

The aim of the present study was to explore the feasibility of using deep learning, such as artificial intelligence (AI), to classify cervical squamous epithelial lesions (SILs) from colposcopy images combined with human papilloma virus (HPV) types. Among 330 patients who underwent colposcopy and biopsy performed by gynecological oncologists, a total of 253 patients with confirmed HPV typing tests were enrolled in the present study. Of these patients, 210 were diagnosed with high-grade SIL (HSIL) and 43 were diagnosed with low-grade SIL (LSIL). An original AI classifier with a convolutional neural network catenating with an HPV tensor was developed and trained. The accuracy of the AI classifier and gynecological oncologists was 0.941 and 0.843, respectively. The AI classifier performed better compared with the oncologists, although not significantly. The sensitivity, specificity, positive predictive value, negative predictive value, Youden's J index and the area under the receiver-operating characteristic curve ± standard error for AI colposcopy combined with HPV types and pathological results were 0.956 (43/45), 0.833 (5/6), 0.977 (43/44), 0.714 (5/7), 0.789 and 0.963±0.026, respectively. Although further study is required, the clinical use of AI for the classification of HSIL/LSIL by both colposcopy and HPV type may be feasible.

New method for determining fibrinogen and FDP threshold criteria by artificial intelligence in cases of massive hemorrhage during delivery.

AIM: To investigate the feasibility of a novel method using artificial intelligence (AI), in which the fibrinogen criterion was determined by the quantitative relation between the distributions of fibrin/fibrinogen degradation products (FDPs) and fibrinogen. METHODS: A dataset of 154 deliveries comprising more than 2000 g of blood lost due to hemorrhage, excluding disseminated intravascular coagulation (DIC), among patients from eight national perinatal centers in Japan from 2011 to 2015 were obtained. The fibrinogen threshold criterion was identified by using the function that best fit the distributions of FDP as determined by AI. FDP production was described by differential equations using a dataset containing fibrinogen levels less than the fibrinogen criterion and solved numerically. RESULTS: A fibrinogen level of 237 mg/dL as the threshold criterion was obtained. The FDP threshold criteria were 2.0 and 8.5 mg/dL for no coagulopathy and a failed coagulation system, respectively. CONCLUSION: The fibrinogen threshold criterion for patients with massive hemorrhage excluding DIC at delivery were obtained by selecting the functions that best fit the distributions of FDP data by using AI.

Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images.

The aim of the present study was to explore the feasibility of using deep learning as artificial intelligence (AI) to classify cervical squamous epithelial lesions (SIL) from colposcopy images. A total of 330 patients who underwent colposcopy and biopsy by gynecologic oncologists were enrolled in the current study. A total of 97 patients received a pathological diagnosis of low-grade SIL (LSIL) and 213 of high-grade SIL (HSIL). An original AI-classifier with 11 layers of the convolutional neural network was developed and trained. The accuracy, sensitivity, specificity and Youden's J index of the AI-classifier and oncologists for diagnosing HSIL were 0.823 and 0.797, 0.800 and 0.831, 0.882 and 0.773, and 0.682 and 0.604, respectively. The area under the receiver-operating characteristic curve was 0.826±0.052 (mean ± standard error), and the 95% confidence interval 0.721-0.928. The optimal cut-off point was 0.692. Cohen's Kappa coefficient for AI and colposcopy was 0.437 (P<0.0005). The AI-classifier performed better than oncologists, although not significantly. Although further study is required, the clinical use of AI for the classification of HSIL/LSIL from by colposcopy may be feasible.

Feasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by age.

PURPOSE: To identify the multivariate logistic regression in a combination (combination method) involving artificial intelligence (AI) classifiers in images of blastocysts along with a conventional embryo evaluation (CEE) to predict the probability of accomplishing a live birth in patients classified by maternal age. METHODS: Retrospectively, a total of 5691 blastocysts were enrolled. Images captured 115 hours or 139 hours if not yet sufficiently large after insemination were classified according to age as follows: <35, 35-37, 38-39, 40-41, and ≥42 years old. The classifiers for each category were created by using convolutional neural networks associated with deep learning. Next, the feasibility of a method combining AI with multivariate logistic model functions by CEE was investigated. RESULTS: The values of the area under the curve (AUC) and the accuracies to predict live birth achieved by the CEE/AI/combination methods were 0.651/0.634/0.655, 0.697/0.688/0.723, 0.771/0.728/0.791, 0.788/0.743/0.806 and 0.820/0.837/0.888, and 0.631/0.647/0.616, 0.687/0.675/0.671, 0.725/0.697/0.732, 0.714/0.776/0.801, and 0.910/0.866/0.784 for age categories of <35, 35-37, 38-39, 40-41, and ≥42 years old, respectively. CONCLUSIONS: Though there were mostly no significant differences regarding the AUC and the sensitivity plus specificity in all age categories, the combination method seemed to be the best.

Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image.

PURPOSE: To make the artificial intelligence (AI) classifiers of the image of the blastocyst implanted later in order to predict the probability of achieving live birth. METHODS: A system for using the machine learning approaches, which are logistic regression, naive Bayes, nearest neighbors, random forest, neural network, and support vector machine, of artificial intelligence to predict the probability of live birth from a blastocyst image was developed. Eighty images of blastocysts that led to live births and 80 images of blastocysts that led to aneuploid miscarriages were used to create an AI-based method with 5-fold cross-validation retrospectively for classifying embryos. RESULTS: The logistic regression method showed the best results. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.65, 0.60, 0.70, 0.67, and 0.64, respectively. Area under the curve was 0.65 ± 0.04 (mean ± SE). Estimated probability of belonging to the live birth category was found significantly related to the probability of live birth (P < 0.005). CONCLUSIONS: Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome.

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age.

PURPOSE: To identify artificial intelligence (AI) classifiers in images of blastocysts to predict the probability of achieving a live birth in patients classified by age. Results are compared to those obtained by conventional embryo (CE) evaluation. METHODS: A total of 5691 blastocysts were retrospectively enrolled. Images captured 115 hours after insemination (or 139 hours if not yet large enough) were classified according to maternal age as follows: <35, 35-37, 38-39, 40-41, and ≥42 years. The classifiers for each category and a classifier for all ages were related to convolutional neural networks associated with deep learning. Then, the live birth functions predicted by the AI and the multivariate logistic model functions predicted by CE were tested. The feasibility of the AI was investigated. RESULTS: The accuracies of AI/CE for predicting live birth were 0.64/0.61, 0.71/0.70, 0.78/0.77, 0.81/0.83, 0.88/0.94, and 0.72/0.74 for the age categories <35, 35-37, 38-39, 40-41, and ≥42 years and all ages, respectively. The sum value of the sensitivity and specificity revealed that AI performed better than CE (P = 0.01). CONCLUSIONS: AI classifiers categorized by age can predict the probability of live birth from an image of the blastocyst and produced better results than were achieved using CE.

Does local infiltration anesthesia on laparoscopic surgical wounds reduce postoperative pain? Randomized control study.

PURPOSE: Recently, endoscopic surgeries are widely performed in the gynecological field. Several studies on the use of local anesthesia for pain control after laparoscopic surgery have been conducted; however, its effects remain controversial. Herein, a randomized control study on gynecological laparoscopic surgeries was conducted to analyze the effectiveness of local anesthesia on postoperative pain. METHODS: Patients who underwent laparoscopic surgeries due to gynecologic benign diseases or endometrial cancer in the early stage were enrolled, and randomly divided into intervention (injected with levobupivacaine), and control (injected with saline) groups. The primary outcome was the dosage of analgesic consumption within 12 hours postoperatively. RESULTS: A total of 147 patients were enrolled in the intervention group and 147 in the control group. The outcome of local anesthesia was not significantly different between the two groups during the whole analysis. We analyzed the effects of local anesthesia in the laparoscopic surgery subgroup. The dosage of analgesic consumption within 12 h after a laparoscopic hysterectomy (TLH) or TLH with pelvic lymph node dissection (TLH+PLD) in the intervention group was significantly smaller than that in the control group. CONCLUSION: Local infiltration anesthesia can effectively reduce postoperative pain in patients who underwent TLH or TLH +PLD.