OBJECTIVE:To evaluate fitting success, continuation of use, and adoption patterns of patient-specific pessaries among individuals with prior unsuccessful or suboptimal outcomes with standard pessaries. METHODS:We conducted a retrospective case series of patients who underwent fitting of a patient-specific pessary between February 2024 and September 2025 across 18 clinical sites in Canada. All fittings, design selections, and outcomes were recorded in a centralized cloud-based platform. Eligibility included symptomatic pelvic organ prolapse with current or prior pessary use or previous unsuccessful fittings. Clinicians could design up to two pessaries per round with iterative refinement. The primary outcome was successful use , defined as the patient's decision to retain and continue using the pessary after up to two fitting rounds and a 4-week home trial. RESULTS:A total of 113 patients underwent fitting of a patient-specific pessary. Initial fitting success was achieved in 87 patients (77.0%, 95% CI, 68.5-83.7%). Of these, 69 (79.3%, 95% CI, 69.7-86.5%) elected to continue using the device after the home trial. Twenty-six patients (23.0%) were not successfully fitted in the first round, of whom seven pursued a second round; four were successfully fitted, and three continued use after the home trial. Overall, 72 of 113 patients (63.7%, 95% CI, 54.5-72.1%) retained and continued using their patient-specific pessary at the final follow-up. CONCLUSION:Patient-specific pessaries achieved high fitting success and continuation rates among individuals who previously had unsuccessful or suboptimal outcomes with standard pessaries. These findings support the clinical utility of individualized devices as a nonsurgical treatment option for pelvic organ prolapse.

BACKGROUND:Transperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with a variety of pelvic floor disorders, including pelvic organ prolapse (POP). Currently, calculating measurements of anatomical structures and relationships as well as extracting the mid-sagittal (MS) plane of 2D and 3D ultrasound images are obtained manually, which is a time-consuming process and requires a reviewer with prior training in pelvic floor US interpretation. The need for manual analysis of ultrasound images has limited the broader adoption of TPUS for evaluating pelvic floor disorders in both research and clinical practice. An automated segmentation and plane extraction method would improve the ability to easily quantify pelvic anatomy relevant to pelvic floor disorders and improve the efficiency and reproducibility of POP diagnosis and treatment. PURPOSE:To develop a fast, reproducible, and automated method of acquiring the MS plane, plane of minimal hiatal dimensions (PMHD), and segmentations of the pelvic floor organs from 3D TPUS images. METHODS:Our method used a nnU-Net segmentation model to segment structures of interest in the 3D TPUS images. The model segmented the pubis symphysis (PS), urethra, bladder, rectum, rectal ampulla, and anorectal angle (ANA). The segmented output was then fed into a heuristics-based method to determine the PS and ANA to extract the MS plane and PMHD automatically. We used a dataset consisting of 161 3D TPUS images from 104 patients. 89 of the volumes were acquired in a resting state and 72 during the Valsalva maneuver. The segmentation and plane extraction algorithms were evaluated by comparing the results with manual segmentations and manual plane extraction methods using the dice similarity coefficients (DSC), mean absolute surface distance (MAD), and absolute angle difference (AAD), respectively. The Wilcoxon-signed rank statistical test was used with Bonferroni-correction to p < 0.01. Cohen effect size was used for comparing model results. RESULTS:The nnU-Net segmentation model reported an average DSC(%) of 70.4%, 58.5%, 57.1%, 48.9%, 39.0%, and 19.8% for bladder, rectum, PS, urethra, ANA, and rectal ampulla respectively. The nnU-Net segmentation model achieved significantly higher DSC (p < 0.01) for the urethra and rectum than all other tested models. Across all metrics, the nnU-Net segmentation model achieved an average effect size of 0.3, 0.5, 0.7, and 0.8 compared to a 3D ResNet34 + U-Net, 3D U-Net, 2D U-Net, and Attention 3D U-Net model, respectively. The average AADs between the automatically calculated plane slices and manually estimated planes dataset for the MS plane and PMHD were 3.8° and 2.4°, respectively. The PS and ANA segmentation centroids were used to calculate the MS plane and PMHD and they had distance errors of 3.6 mm and 4.4 mm. CONCLUSIONS:We developed an automated 3D segmentation and multiple plane extraction method of female pelvic floor 3D US images. Our method extracts the MS plane and PMHD from 3D US images. The proposed algorithm pipeline can improve the efficiency and reproducibility of TPUS analysis for pelvic floor disorder diagnosis and treatment.

Introduction Levator ani muscle (LAM) avulsion is a common traumatic injury of the pelvic floor muscle occurring during vaginal childbirth and is linked to the development of pelvic organ prolapse (POP). POP is a pelvic floor disorder that affects up to 40% of women during their lifetime. Pelvic floor ultrasound imaging is used to diagnose LAM avulsion, but it requires trained experts and is time-consuming, leading to weeks-long delays in receiving diagnostic results and treatment. The purpose of this study is to demonstrate the feasibility of a deep learning system to automatically classify the degree of LAM avulsion from 3D transperineal ultrasound images (TPUS). Methods 3D TPUS images of the pelvic floor from 150 patients with and without POP-related LAM avulsion were collected. Out of these, 113 patients were included in the study. Over 650 key slices were extracted from the ultrasound volumes and cropped to a region of interest. A two-stage cascading ensemble architecture was developed, combining three convolutional neural networks (MobileNetV3-Small, EfficientNet-B0, and RegNetY-800MF) with a final decision layer. The system performs hierarchical classification: first distinguishing between normal and avulsion cases, then determining unilateral versus bilateral involvement, and finally classifying the degree of avulsion. Results In 5-fold cross-validation, the ensemble model demonstrated strong performance in both binary classification tasks. For avulsion detection, it achieved 86% accuracy, 88% sensitivity, 85% specificity, and an AUC of 0.94, consistently outperforming individual base classifiers (which achieved AUCs of 0.71 to 0.75). For bilateral/unilateral classification, the model achieved 80% accuracy, 82% sensitivity, 78% specificity, and an AUC of 0.87. When evaluated on a test set for final patient-level classification across five classes (normal, complete bilateral avulsion, complete unilateral avulsion, partial bilateral avulsion, and partial unilateral avulsion), the system achieved 46% accuracy. Conclusion This study demonstrates the feasibility of a deep learning classification system to automatically classify the degree of LAM avulsion from 3D TPUS images. While the system showed promising performance in binary classification tasks, its sequential decision-making design means that a single slice misclassification in the first classification stage can impact the final patient-level accuracy. Additionally, the limited dataset size can hinder the model's ability to generalize effectively to unseen cases. Despite these limitations, the developed system shows the potential to expedite LAM avulsion diagnosis, overcoming the time constraints of manual diagnosis. This approach can broaden screening access, benefiting areas with limited healthcare resources, by reducing expert reliance and enabling timely treatment. Future work with larger and more diverse datasets could help address current limitations and further improve classification accuracy.

BACKGROUND:The distension properties of the vagina are critical to its function including support of surrounding organs, childbirth, and intercourse. It could be altered by many pathophysiological processes like pregnancy, radiotherapy, and reconstruction surgery. However, there are no clinically available diagnostic tools capable of quantifying the distension properties of the vagina.PURPOSE:A proof-of-concept study was designed to assess the feasibility of a novel three-dimensional (3D) ultrasound imaging technique that allows quantitative evaluation of the vagina under distension.METHODS:Patients with symptomatic pelvic organ prolapse (POP) were recruited for the study. An ultrathin, oversized bag was inserted into the vagina and filled with water using a modified urodynamics system. The instilled water volume and intravaginal pressure were continuously recorded. At maximum vaginal capacity, 3D transintroital ultrasound of the distended vagina and surrounding pelvic structures was performed. Exams were performed in duplicate for each patient, two hours apart (round A and round B). Following the development of a 3D surface model of the distended vagina from each scan, several measurements were obtained, including cross-sectional area, anteroposterior (AP) length and lateral width in the plane of minimum hiatal dimensions (PMHD), AP and lateral diameter at the pubic symphysis (PS) level, maximum and minimum diameter, and maximum vertical length. To assess repeatability between measurements in two rounds, the coefficient of variation (CV) and the intraclass correlation coefficient (ICC) were calculated for each measurement. Correlations between physical measurements including the pelvic organ prolapse quantification (POP-Q) system and vaginal diameter measurements, and obtained metrics were also assessed.RESULTS:Sixteen patients with POP (average age 69 years) completed both rounds of imaging. There was sufficient echogenicity on 3D transintroital ultrasound of the distended vaginal wall to establish boundaries for 3D surface models of the vagina. Overall, all metrics had good or excellent reliability (ICC = 0.77-0.93, p < 0.05; CV = 3%-18%) except maximum diameter, which demonstrated only moderate reliability (ICC = 0.67, p = 0.092). Strong correlations were found between physical exam measurements including D point of POP-Q, introitus diameter and lateral diameter at apex, and maximum vaginal capacity, maximum vertical length, lateral diameter at PS, minimum diameter, and distended PMHD measurements. The results demonstrated that this system could generate 3D models of the shape of the distended vagina and provide multiple metrics that could be reliably calculated from automated analyses of the models.CONCLUSIONS:A novel system for evaluation of the distension properties of the vagina was developed and preliminary evaluation was performed. This system may represent a technique for evaluation of the biomechanical and structural properties of the vagina.

Importance Vaginal pessaries are an effective nonsurgical treatment for pelvic organ prolapse (POP) when properly fitted. However, pessary fitting and use are often unsuccessful or imperfect. Objective The objective of this study was to assess the feasibility of using patient-specific pessaries fabricated from three-dimensional (3D)-printed molds to improve POP symptoms and increase overall satisfaction of pessary treatment in patients using standard vaginal pessaries. Study Design Patients undergoing POP treatment with standard vaginal pessaries were enrolled in this pilot prospective study. Patient-specific pessaries were designed and fabricated for each patient using patient input, physician input, and anatomic measurements from clinical assessment. Pessary fabrication involved injection of biocompatible liquid silicone rubber into 3D-printed molds followed by a biocompatible silicone coating. Pelvic organ prolapse symptomatic distress and pessary treatment satisfaction were evaluated before and after a 3-week patient-specific pessary home trial using the validated Pelvic Organ Prolapse Distress Inventory-6 form and a visual analog scale, respectively. Results Eight women were included in this study. Changing from standard pessary to patient-specific pessary treatment was associated with an improvement in prolapse symptoms on the Pelvic Organ Prolapse Distress Inventory-6 (median change, −3.5; interquartile range, −5 to −2.5; P = 0.02) and an increase in overall pessary satisfaction on a visual analog scale (median change, +2.0; interquartile range, +1.0 to +3.0; P = 0.02). All patients reported either an improvement or no change in pessary ease of use, comfort, and the feeling of support provided by the pessary. Conclusion Patient-specific vaginal pessaries are a promising alternative to standard pessaries for alleviating POP symptoms and improving patient satisfaction with pessary use.

Three-dimensional (3D) transperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with a variety of pelvic floor disorders, including pelvic organ prolapse (POP). Patients with POP have abnormal descent of one or more pelvic organs (i.e., bladder, uterus, vagina) through the levator hiatus, which is often experienced by the patient as a persistent bothersome bulge protruding from the vaginal opening. The enlargement of the hiatal opening measured in the plane of minimal hiatal dimensions (PMHD), has been used as an indication for POP severity. Manually measuring the size of the levator hiatus in 3D TPUS images can be challenging and requires expertise and training and is timeconsuming. Hence a fully automated method for estimating the dimensions of hiatal opening is highly desirable. To this end, we developed a fully automated method to segment the levator hiatus from the PMHD based on the nnU-Net model framework. We trained, validated, and tested on a total of 252 3D US images from 138 patients that may have POP as determined by the pelvic organ prolapse quantification (POP-Q) system. As a benchmark comparison, we compared the nnU-Net to a vanilla U-Net whose hyperparameters were manually tuned. Model performance was determined using Dice similarity coefficient (DSC) and levator hiatus width, length, and area by comparing the model segmentations to manual segmentations. The nnU-Net achieved a DSC of 93.1%±3.3%, absolute width difference of 2.3mm±1.7mm, absolute length difference of 2.6mm±2.5mm and absolute area difference of 1.8cm2±1.3cm2.

BackgroundTransperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with pelvic floor disorders, including pelvic organ prolapse (POP). Currently, measurements of anatomical structures in the mid-sagittal plane of 2D and 3D US volumes are obtained manually, which is time-consuming, has high intra-rater variability, and requires an expert in pelvic floor US interpretation. Manual segmentation and biometric measurement can take 15 min per 2D mid-sagittal image by an expert operator. An automated segmentation method would provide quantitative data relevant to pelvic floor disorders and improve the efficiency and reproducibility of segmentation-based biometric methods. PurposeDevelop a fast, reproducible, and automated method of acquiring biometric measurements and organ segmentations from the mid-sagittal plane of female 3D TPUS volumes. MethodsOur method used a nnU-Net segmentation model to segment the pubis symphysis, urethra, bladder, rectum, rectal ampulla, and anorectal angle in the mid-sagittal plane of female 3D TPUS volumes. We developed an algorithm to extract relevant biometrics from the segmentations. Our dataset included 248 3D TPUS volumes, 126/122 rest/Valsalva split, from 135 patients. System performance was assessed by comparing the automated results with manual ground truth data using the Dice similarity coefficient (DSC) and average absolute difference (AD). Intra-class correlation coefficient (ICC) and time difference were used to compare reproducibility and efficiency between manual and automated methods respectively. High ICC, low AD and reduction in time indicated an accurate and reliable automated system, making TPUS an efficient alternative for POP assessment. Paired t-test and non-parametric Wilcoxon signed-rank test were conducted, with p p values (in brackets), compared to the next best tested model, of 87.4% (<0.0001), 68.5% (<0.0001), 61.0% (0.1), 54.6% (0.04), 49.2% (<0.0001) and 33.7% (0.02) for bladder, rectum, urethra, pubic symphysis, anorectal angle, and rectal ampulla respectively. The average ADs for the bladder neck position, bladder descent, rectal ampulla descent and retrovesical angle were 3.2 mm, 4.5 mm, 5.3 mm and 27.3 degrees, respectively. The biometric algorithm had an ICC > 0.80 for the bladder neck position, bladder descent and rectal ampulla descent when compared to manual measurements, indicating high reproducibility. The proposed algorithms required approximately 1.27 s to analyze one image. The manual ground truths were performed by a single expert operator. In addition, due to high operator dependency for TPUS image collection, we would need to pursue further studies with images collected from multiple operators. ConclusionsBased on our search in scientific databases (i.e., Web of Science, IEEE Xplore Digital Library, Elsevier ScienceDirect and PubMed), this is the first reported work of an automated segmentation and biometric measurement system for the mid-sagittal plane of 3D TPUS volumes. The proposed algorithm pipeline can improve the efficiency (1.27 s compared to 15 min manually) and has high reproducibility (high ICC values) compared to manual TPUS analysis for pelvic floor disorder diagnosis. Further studies are needed to verify this system's viability using multiple TPUS operators and multiple experts for performing manual segmentation and extracting biometrics from the images.

There is an increasing interest in the applications of 3D ultrasound imaging of the pelvic floor to improve the diagnosis, treatment, and surgical planning of female pelvic floor dysfunction (PFD). Pelvic floor biometrics are obtained on an oblique image plane known as the plane of minimal hiatal dimensions (PMHD). Identifying this plane requires the detection of two anatomical landmarks, the pubic symphysis and anorectal angle. The manual detection of the anatomical landmarks and the PMHD in 3D pelvic ultrasound requires expert knowledge of the pelvic floor anatomy, and is challenging, time-consuming, and subject to human error. These challenges have hindered the adoption of such quantitative analysis in the clinic. This work presents an automatic approach to identify the anatomical landmarks and extract the PMHD from 3D pelvic ultrasound volumes. To demonstrate clinical utility and a complete automated clinical task, an automatic segmentation of the levator-ani muscle on the extracted PMHD images was also performed. Experiments using 73 test images of patients during a pelvic muscle resting state showed that this algorithm has the capability to accurately identify the PMHD with an average Dice of 0.89 and an average mean boundary distance of 2.25mm. Further evaluation of the PMHD detection algorithm using 35 images of patients performing pelvic muscle contraction resulted in an average Dice of 0.88 and an average mean boundary distance of 2.75mm. This work had the potential to pave the way towards the adoption of ultrasound in the clinic and development of personalized treatment for PFD.

Acoustic microscopy (AM) provides micro-meter resolution using a highly focused single-element transducer. A drawback in AM is a relatively small depth of field, resulting in poor resolution outside the focus. Synthetic aperature focusing technique (SAFT) can be used to improve the image resolution throughout the field of view. SAFT mathematically synthesizes the effect of an array transducer and produces dynamic focusing and depth-independent resolution. SAFT in time domain with and without apodization, TD-SAFT and ATD-SAFT, respectively, and in frequency domain (FD-SAFT) were implemented and tested using simulated and experimental radio-frequency data from an acoustic microscope at 400 MHz. Lateral resolution of all the SAFT reconstructed images were better than those of the conventional B-mode images. While TD-SAFT and FD-SAFT permformed better than ATD-SAFT in improving the lateral resolution, ATD-SAFT provided lower side lobes. In conclusion, SAFT improves resolution in AM outside the focal region.

Female pelvic floor dysfunction may manifest as pelvic organ prolapse (POP), urinary or fecal incontinence, pelvic pain or chronic constipation. POP is the descent of the pelvic organs into the vaginal cavity, affecting up to 50% of the female population. Diagnostic evaluation of POP is often performed via clinical examination (i.e. palpation). However, clinical examination is inefficient to assess structural abnormalities. There has been an increasing interest in the applications of ultrasound imaging for pelvic floor imaging to better understand the pathophysiology of pelvic floor dysfunction and POP. This is in part due to the recent developments in three-dimensional (3D) and 4D ultrasound imaging. However, despite its wide application in research, pelvic floor 3D ultrasound has not been employed in the clinic for the assessment of POP, which is likely due to the high cost of 3D ultrasound imaging systems. In this work, a cost-effective technique for acquiring 3D pelvic floor ultrasound images using a conventional 2D curvilinear probe is presented and compared against commercial 3D probes. This is achieved by a hand-held, mechanically-assisted 3D ultrasound scanner. This system has potential to decrease the cost of 3D pelvic floor ultrasound imaging and increase its application for POP assessment.

The publisher regrets that an error was present in the title of the above article. The correct title appears above. The publisher would like to apologise for any inconvenience caused. Quantitative Analysis of Needle Navigation under Ultrasound Guidance in s Simulated Central Venous Line ProcedureUltrasound in Medicine and BiologyVol. 44Issue 8PreviewComplications in ultrasound-guided central line insertions are associated with the expertise level of the operator. However, a lack of standards for teaching, training and evaluation of ultrasound guidance results in various levels of competency during training. To address such shortcomings, there has been a paradigm shift in medical education toward competency-based training, promoting the use of simulators and quantitative skills assessment. It is therefore necessary to develop reliable quantitative metrics to establish standards for the attainment and maintenance of competence. Full-Text PDF

Applications of ultrasound guidance for epidural injections are hindered by poor needle and epidural space visualization. This work presents an augmented reality (AR) ultrasound guidance system that addresses challenges in both needle visualization during navigation and epidural space identification for needle positioning. In this system, (i) B-mode ultrasound and the needle are visualized in a 3-D AR environment for improved navigation, and (ii) A-mode ultrasound, obtained from a custom-made single-element transducer housed at the needle tip, is used to identify the epidural space for improved needle positioning. Performance of the system was evaluated against ultrasound-only guidance in a phantom study with novice operators and an expert anesthesiologist. The procedure success rate was higher with the AR system (100%) than ultrasound-only guidance (57%). The AR system has the potential to improve procedure outcomes in terms of success rate, time, needle path-length and usability.

Complications in ultrasound-guided central line insertions are associated with the expertise level of the operator. However, a lack of standards for teaching, training and evaluation of ultrasound guidance results in various levels of competency during training. To address such shortcomings, there has been a paradigm shift in medical education toward competency-based training, promoting the use of simulators and quantitative skills assessment. It is therefore necessary to develop reliable quantitative metrics to establish standards for the attainment and maintenance of competence. This work identifies such a metric for simulated central line procedures. The distance between the needle tip and ultrasound image plane was quantified as a metric of efficacy in ultrasound guidance implementation. In a simulated procedure, performed by experienced physicians, this distance was significantly greater in unsuccessful procedures (p = 0.04). The use of this metric has the potential to enhance the teaching, training and skills assessment of ultrasound-guided central line insertions.

Epidural anesthesia is one of the most commonly used and yet challenging techniques employed for pain management and anesthesia delivery. The major complications of this procedure are due to accidental dural puncture, with an incidence of 1-3%, which could lead to both temporary and irreversible permanent neurological complications. Needle placement under ultrasound (US) guidance has received increasing interest for improving needle placement accuracy. However, poor needle visibility in US, difficulties in displaying relevant anatomical structure such as dura mater due to attenuation and bone shadowing, and image interpretation variability among users pose significant hurdles for any US guidance system. As a result, US guidance for epidural injections has not been widely adopted for everyday use for the performance of neuraxial blocks. The difficulties in localizing the ligamentum flavum and dura with respect to the needle tip can be addressed by integrating A-mode US, provided by a single-element transducer at the needle tip, into the B-mode US guidance system. We have taken the first steps towards providing such a guidance system. Our goal is to improve the safety of this procedure with minimal changes to the clinical workflow. This work presents the design and development of a 20 MHz single-element US transducer housed at the tip of a 19 G needle hypodermic tube, which can fit inside an epidural introducer needle. In addition, the results from initial transducer characterization tests and performance evaluation of the transducer in a euthanized porcine model are provided.

Ultrasound (US)-guided interventions are often enhanced via integration with an augmented reality environment, a necessary component of which is US calibration. Calibration requires the segmentation of fiducials, i.e., a phantom, in US images. Fiducial localization error (FLE) can decrease US calibration accuracy, which fundamentally affects the total accuracy of the interventional guidance system. Here, we investigate the effects of US image reconstruction techniques as well as phantom material and geometry on US calibration. It was shown that the FLE was reduced by 29% with synthetic transmit aperture imaging compared with conventional B-mode imaging in a Z-bar calibration, resulting in a 10% reduction of calibration error. In addition, an evaluation of a variety of calibration phantoms with different geometrical and material properties was performed. The phantoms included braided wire, plastic straws, and polyvinyl alcohol cryogel tubes with different diameters. It was shown that these properties have a significant effect on calibration error, which is a variable based on US beamforming techniques. These results would have important implications for calibration procedures and their feasibility in the context of image-guided procedures.

PURPOSE:Real-time ultrasound has become a crucial aspect of several image-guided interventions. One of the main constraints of such an approach is the difficulty in interpretability of the limited field of view of the image, a problem that has recently been addressed using mixed reality, such as augmented reality and augmented virtuality. The growing popularity and maturity of mixed reality has led to a series of informal guidelines to direct development of new systems and to facilitate regulatory approval. However, the goals of mixed reality image guidance systems and the guidelines for their development have not been thoroughly discussed. The purpose of this paper is to identify and critically examine development guidelines in the context of a mixed reality ultrasound guidance system through a case study.METHODS:A mixed reality ultrasound guidance system tailored to central line insertions was developed in close collaboration with an expert user. This system outperformed ultrasound-only guidance in a novice user study and has obtained clearance for clinical use in humans. A phantom study with 25 experienced physicians was carried out to compare the performance of the mixed reality ultrasound system against conventional ultrasound-only guidance. Despite the previous promising results, there was no statistically significant difference between the two systems.RESULTS:Guidelines for developing mixed reality image guidance systems cannot be applied indiscriminately. Each design decision, no matter how well justified, should be the subject of scientific and technical investigation. Iterative and small-scale evaluation can readily unearth issues and previously unknown or implicit system requirements.CONCLUSIONS:We recommend a wary eye in development of mixed reality ultrasound image guidance systems emphasizing small-scale iterative evaluation alongside system development. Ultimately, we recommend that the image-guided intervention community furthers and deepens this discussion into best practices in developing image-guided interventions.