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This research aimed to assess the validity of ultrasound scans with new features in detecting fetal anal atresia and verify the effectiveness of these new features. Additionally, we aimed at investigating the perinatal incidence of anal atresia. This multicenter prospective study recruited 94,617 normal gravidas and 84 gravidas with anal atresia fetuses. The gold standard for diagnosing perinatal anal atresia is routine neonatal anus examinations. The incidence calculation was based on the results of the gold standard. The validity of our new approach was evaluated via a diagnostic test involving all 94,701 subjects. The effectiveness of our new features was assessed through an ablation study in a randomly established new dataset, with the ratio of anal atresia to non-anal atresia cases of 1:4. The annual perinatal incidence of anal atresia between 2019 and 2023 ranges from 0.57‰ to 1.29‰. Our new method performed great regarding the Youden index, diagnostic odds ratio (DOR), area under the curve (AUC) of the receiver operating characteristic curve (ROCC), AUC of the precision-recall curve (PRC), F1-score, and Cramer’s V. In the ablation study, our new approach surpassed its competitors concerning Youden index, DOR, AUC of the ROCC, and AUC of the PRC. Ultrasound scans show high validity and clinical value in detecting fetal anal atresia. Our new ultrasound features significantly promote the detection of fetal anal atresia. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-73524-3.
This research aimed to assess the validity of ultrasound scans with new features in detecting fetal anal atresia and verify the effectiveness of these new features. Additionally, we aimed at investigating the perinatal incidence of anal atresia. This multicenter prospective study recruited 94,617 normal gravidas and 84 gravidas with anal atresia fetuses. The gold standard for diagnosing perinatal anal atresia is routine neonatal anus examinations. The incidence calculation was based on the results of the gold standard. The validity of our new approach was evaluated via a diagnostic test involving all 94,701 subjects. The effectiveness of our new features was assessed through an ablation study in a randomly established new dataset, with the ratio of anal atresia to non-anal atresia cases of 1:4. The annual perinatal incidence of anal atresia between 2019 and 2023 ranges from 0.57‰ to 1.29‰. Our new method performed great regarding the Youden index, diagnostic odds ratio (DOR), area under the curve (AUC) of the receiver operating characteristic curve (ROCC), AUC of the precision-recall curve (PRC), F1-score, and Cramer’s V. In the ablation study, our new approach surpassed its competitors concerning Youden index, DOR, AUC of the ROCC, and AUC of the PRC. Ultrasound scans show high validity and clinical value in detecting fetal anal atresia. Our new ultrasound features significantly promote the detection of fetal anal atresia. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-73524-3.
This article introduces the Modified Al-Biruni Earth Radius (MBER) algorithm, which seeks to improve the precision of categorizing eye states as either open (0) or closed (1). The evaluation of the proposed algorithm was assessed using an available EEG dataset that applied preprocessing techniques, including scaling, normalization, and elimination of null values. The MBER algorithm’s binary format is specifically designed to select features that can significantly enhance the accuracy of classification. The proposed algorithm and competing ones, namely, Al-Biruni Earth Radius (BER), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WAO), Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA) were evaluated using predefined sets of assessment criteria. The statistical analysis employed the ANOVA and Wilcoxon signed-rank tests and assessed the effectiveness and significance of the proposed algorithm compared to the other five algorithms. Furthermore, A series of visual depictions were presented to validate the effectiveness and robustness of the proposed algorithm. Thus, the MBER algorithm outperformed the other optimizers on the majority of the unimodal benchmark functions due to these considerations. Different ML models were used for classification, e.g., DT, RF, KNN, SGD, GNB, SVC, and LR. The KNN model achieved the highest values of Precision (PPV) (0.959425), Negative Predictive Value (NPV) (0.964969), FScore (0.963431), accuracy (0.9612), Sensitivity (0.970578) and Specificity (0.949711). Thus, KNN serves as a fitness function and is optimized by the utilization of Modified Al-Biruni earth radius (MBER). Finally, the accuracy of eye state classification achieved 96.12% using the proposed algorithm. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-74475-5.
Purpose This study aimed to compare the cost-effectiveness of AI-based approaches with manual approaches in ultrasound image quality control (QC). Methods Eligible ultrasonographers and pregnant volunteers were prospectively recruited from the Hunan Maternal and Child Health Hospital in May 2023. The ultrasonographers were randomly and evenly assigned to either the AI or Manual QC groups with baseline scores determined in June-July. From August to October, these groups received real-time AI or post-scan manual QC with post-interventional scores recorded monthly. We applied the repeated measures analysis of variance to analyze the between-subject and within-subject effectiveness and time trends in effectiveness (QC score improvement) assessment. An extra 50 pregnant volunteers underwent real-time manual QC, with their screening images utilized for post-scan AI and manual QC. The time cost of real-time AI QC was zero since it only required trainees’ involvement. We used Friedman’s M and Quade tests to compare multiple independent medians in cost assessment. Results This study recruited 14 ultrasonographers, equally divided into the AI and Manual QC groups. No significant difference existed between the groups concerning age, service year in perinatal diagnosis, male proportion, and QC frequency. The simple effect of the group revealed that the AI QC method outperformed the Manual QC method at least once ( F = 13.113, P = 0.004, η 2 = 0.522). The simple effect of the month in the AI QC groups indicated an improvement in the mean QC scores ( F = 9.827, P = 0.003, η 2 = 0.747) while that of manual QC groups suggested no improvement ( F = 0.144, P = 0.931, η 2 = 0.041). Baseline scores were equal in June-July ( F = 0.031, P = 0.864, η 2 = 0.003). However, the AI QC group surpassed the Manual QC group in August ( F = 14.579, P = 0.002, η 2 = 0.549), September ( F = 28.590, P < 0.001, η 2 = 0.704), and October ( F = 35.411, P < 0.001, η 2 = 0.747). Within the Manual QC group, no significant differences were found in scores between June-July and August, September, or October (all P values of 1.000, nominal significance level of 0.0083). In contrast, the AI QC group showed significantly higher scores in August, September, and October co...
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