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

Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis

The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across various organs while reducing physician workload. AI's error detection capabilities further enhance diagnostic accuracy. Looking ahead, the integration of AI with ultrasound is expected to deepen, promoting trends in standardization, personalized treatment, and intelligent healthcare, particularly in underserved areas. Despite its potential, comprehensive assessments of AI's diagnostic accuracy and ethical implications remain limited, necessitating rigorous evaluations to ensure effectiveness in clinical practice. This review provides a systematic evaluation of AI technologies in ultrasound medicine, highlighting their transformative potential to improve global healthcare outcomes.

Automatic anal sphincter integrity detection from ultrasound images via convolutional neural networks

BACKGROUND

The anal sphincter complex comprises the anal sphincter and the U-shaped deep and superficial puborectalis muscle. As an important supporting structure of the posterior pelvic floor, together with its surrounding tissues and muscles, the anal sphincter complex maintains the normal physiological functions of defecation and continence.

OBJECTIVE

The plane required for diagnosing anal sphincter injury and the diagnosis of anal sphincter integrity through pelvic floor ultrasound are highly dependent on sonographers' experience. We developed a deep learning (DL) tool for the automatic diagnosis of anal sphincter integrity via pelvic floor ultrasound.

METHODS

A 2D detection network was trained to detect the bounding box of the anal sphincter. The pelvic floor ultrasound image and its corresponding oval mask were input into a 2D classification network to determine the integrity of the anal sphincter. The average precision (AP) and intersection over union (IoU) were used to evaluate the performance of anal sphincter detection. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the classification model.

RESULTS

The Pearson correlation coefficients (r values) of the topmost and bottommost layers detected by the CNN and sonographers were 0.932 and 0.978, respectively. The best DL model yielded the highest area under the curve (AUC) of 0.808 (95% CI: 0.698-0.921) in the test cohort. The results from the CNN agreed well with the diagnostic results of experienced sonographers.

CONCLUSIONS

We proposed, for the first time, a CNN to obtain the plane required for diagnosing anal sphincter injury on the basis of pelvic floor ultrasound and for preliminarily diagnosing anal sphincter injury.

Fully Automated Localization and Measurement of Levator Hiatus Dimensions Using 3-D Pelvic Floor Ultrasound

OBJECTIVE

To develop an algorithm for the automated localization and measurement of levator hiatus (LH) dimensions (AI-LH) using 3-D pelvic floor ultrasound.

METHODS

The AI-LH included a 3-D plane regression model and a 2-D segmentation model, which first achieved automated localization of the minimal LH dimension plane (C-plane) and measurement of the hiatal area (HA) on maximum Valsalva on the rendered LH images, but not on the C-plane. The dataset included 600 volumetric data. We compared AI-LH with sonographer difference (ASD) as well as the inter-sonographer differences (IESD) in the testing dataset (n = 240). The assessment encompassed the mean absolute error (MAE) for the angle and center point distance of the C-plane, along with the Dice coefficient, MAE, and intra-class correlation coefficient (ICC) for HA, and included the time consumption.

RESULTS

The MAE of the C-plane of ASD was 4.81 ± 2.47° with 1.92 ± 1.54 mm. AI-LH achieved a mean Dice coefficient of 0.93 for LH segmentation. The MAE on HA of ASD (1.44 ± 1.12 mm²) was lower than that of IESD (1.63 ± 1.58 mm²). The ICC on HA of ASD (0.964) was higher than that of IESD (0.949). The average time costs of AI-LH and manual measurement were 2.00 ± 0.22 s and 59.60 ± 2.63 s (t = 18.87, p < 0.01), respectively.

CONCLUSION

AI-LH is accurate, reliable, and robust in the localization and measurement of LH dimensions, which can shorten the time cost, simplify the operation process, and have good value in clinical applications.

Validation of an automated software (Smartpelvic™) in assessing hiatal area from three dimensional transperineal pelvic volumes of pregnant women: comparison with manual analysis

OBJECTIVES

The aim of this investigation was to evaluate the agreement between a manual and an automatic technique in assessing levator hiatus area (LHA) during pregnancy from three-dimensional (3D) pelvic floor volumes obtained by trans-perineal ultrasound (TPUS).

METHODS

3D volumes were acquired during rest, maximum pelvic floor contraction and Valsalva maneuver from 66 pregnant women. Manual selection of LHA and automatic software (Smart Pelvic™) were applied on TPUS volume starting from a C-plane view. To evaluate intra- and inter-observer variability measurements of LHA were performed twice by the same operator and once by a second sonographer. Reference hiatal contours obtained manually by the first operator were compared with the automated ones. Reproducibility was evaluated by intraclass correlation coefficients (ICC) and Bland-Altman plots.

RESULTS

LHA measurement, using automatic software, achieved excellent intra-observer and inter-observer reproducibility in pregnant women both at rest and after dynamic analysis (ICC>0.9). Further, an excellent agreement resulted between manual selection of the LHA and automatic imaging (ICC>0.9). The average time taken to obtain LHA manually was significantly longer when compared to the automatic analysis (p≤0.0001).

CONCLUSIONS

Smart pelvic software resulted from a reliable method for automatically measuring the LHA, showing high reproducibility and accuracy.

Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.

Deep learning-based pelvic levator hiatus segmentation from ultrasound images

Purpose

To automatically segment and measure the levator hiatus with a deep learning approach and evaluate the performance between algorithms, sonographers, and different devices.

Methods

Three deep learning models (UNet-ResNet34, HR-Net, and SegNet) were trained with 360 images and validated with 42 images. The trained models were tested with two test sets. The first set included 138 images to evaluate the performance between the algorithms and sonographers. An independent dataset including 679 images assessed the performances of algorithms between different ultrasound devices. Four metrics were used for evaluation: DSC, HDD, the relative error of segmentation area, and the absolute error of segmentation area.

Results

The UNet model outperformed HR-Net and SegNet. It could achieve a mean DSC of 0.964 for the first test set and 0.952 for the independent test set. UNet was creditable compared with three senior sonographers with a noninferiority test in the first test set and equivalent in the two test sets collected by different devices. On average, it took two seconds to process one case with a GPU and 2.4 s with a CPU.

Conclusions

The deep learning approach has good performance for levator hiatus segmentation and good generalization ability on independent test sets. This automatic levator hiatus segmentation approach could help shorten the clinical examination time and improve consistency.