Intra-cavitary uterine pathology in women with abnormal uterine bleeding: a prospective study of 1220 women.

OBJECTIVES: Our primary aim was to assess how patients characteristics, bleeding pattern, sonographic endometrial thickness (ET) and additional features at unenhanced ultrasound examination (UTVS) and at fluid instillation sonography (FIS) contribute to the diagnosis of intracavitary uterine pathology in women presenting with abnormal uterine bleeding (AUB). We further aimed to report the prevalence of pathology in women presenting with AUB. METHODS: 1220 consecutive women presenting with AUB underwent UTVS, colour Doppler imaging (CDI) and FIS. Most women (n = 1042) had histological diagnosis. RESULTS: Mean age was 50 years and 37% were postmenopausal. Of 1220 women 54% were normal, polyps were diagnosed in 26%, intracavitary fibroids in 11%, hyperplasia without atypia in 4% and cancer in 3%. All cancers were diagnosed in postmenopausal (7%) or perimenopausal (1%) women. ET had a low predictive value in premenopausal women (LR+ and LR- of 1.34 and 0.74, respectively), while FIS had a LR+ and LR- of 6.20 and 0.24, respectively. After menopause, ET outperformed all patient characteristics for the prediction of endometrial pathology (LR+ and LR- of 3.13 and 0.24). The corresponding LR+ and LR- were 10.85 and 0.71 for CDI and 8.23 and 0.26 for FIS. CONCLUSION: About half of the women presenting to a bleeding clinic will have pathology. In premenopausal women, benign lesions are often the cause of AUB. For the prediction of intracavitary pathology ET is of little value in premenopausal women. CDI and FIS substantially improve the diagnostic accuracy

Automatic Extraction of Hiatal Dimensions in 3-D Transperineal Pelvic Ultrasound Recordings

The aims of this work were to create a robust automatic software tool for measurement of the levator hiatal area on transperineal ultrasound (TPUS) volumes and to measure the potential reduction in variability and time taken for analysis in a clinical setting. The proposed tool automatically detects the C-plane (i.e., the plane of minimal hiatal dimensions) from a 3-D TPUS volume and subsequently uses the extracted plane to automatically segment the levator hiatus, using a convolutional neural network. The automatic pipeline was tested using 73 representative TPUS volumes. Reference hiatal outlines were obtained manually by two experts and compared with the pipeline's automated outlines. The Hausdorff distance, area, a clinical quality score, C-plane angle and C-plane Euclidean distance were used to evaluate C-plane detection and quantify levator hiatus segmentation accuracy. A visual Turing test was created to compare the performance of the software with that of the expert, based on the visual assessment of C-plane and hiatal segmentation quality. The overall time taken to extract the hiatal area with both measurement methods (i.e., manual and automatic) was measured. Each metric was calculated both for computer–observer differences and for inter-and intra-observer differences. The automatic method gave results similar to those of the expert when determining the hiatal outline from a TPUS volume. Indeed, the hiatal area measured by the algorithm and by an expert were within the intra-observer variability. Similarly, the method identified the C-plane with an accuracy of 5.76 ± 5.06° and 6.46 ± 5.18 mm in comparison to the inter-observer variability of 9.39 ± 6.21° and 8.48 ± 6.62 mm. The visual Turing test suggested that the automatic method identified the C-plane position within the TPUS volume visually as well as the expert. The average time taken to identify the C-plane and segment the hiatal area manually was 2 min and 35 ± 17 s, compared with 35 ± 4 s for the automatic result. This study presents a method for automatically measuring the levator hiatal area using artificial intelligence-based methodologies whereby the C-plane within a TPUS volume is detected and subsequently traced for the levator hiatal outline. The proposed solution was determined to be accurate, relatively quick, robust and reliable and, importantly, to reduce time and expertise required for pelvic floor disorder assessment

Age, weight, body mass index and endometrial thickness in postmenopausal women

[No abstract available]Articl