An end-to-end geometry-based pipeline for automatic preoperative surgical planning of pelvic fracture reduction and fixation.

Computer-assisted preoperative planning of pelvic fracture reduction surgery has the potential to increase the accuracy of the surgery and to reduce complications. However, the diversity of the pelvic fractures and the disturbance of small fracture fragments present a great challenge to perform reliable automatic preoperative planning. In this paper, we present a comprehensive and automatic preoperative planning pipeline for pelvic fracture surgery. It includes pelvic fracture labeling, reduction planning of the fracture, and customized screw implantation. First, automatic bone fracture labeling is performed based on the separation of the fracture sections. Then, fracture reduction planning is performed based on automatic extraction and pairing of the fracture surfaces. Finally, screw implantation is planned using the adjoint fracture surfaces. The proposed pipeline was tested on different types of pelvic fracture in 14 clinical cases. Our method achieved a translational and rotational accuracy of 2.56 mm and 3.31° in reduction planning. For fixation planning, a clinical acceptance rate of 86.7% was achieved. The results demonstrate the feasibility of the clinical application of our method. Our method has shown accuracy and reliability for complex multi-body bone fractures, which may provide effective clinical preoperative guidance and may improve the accuracy of pelvic fracture reduction surgery.

A bidirectional framework for fracture simulation and deformation-based restoration prediction in pelvic fracture surgical planning.

Pelvic fracture is a severe trauma with life-threatening implications. Surgical reduction is essential for restoring the anatomical structure and functional integrity of the pelvis, requiring accurate preoperative planning. However, the complexity of pelvic fractures and limited data availability necessitate labor-intensive manual corrections in a clinical setting. We describe in this paper a novel bidirectional framework for automatic pelvic fracture surgical planning based on fracture simulation and structure restoration. Our fracture simulation method accounts for patient-specific pelvic structures, bone density information, and the randomness of fractures, enabling the generation of various types of fracture cases from healthy pelvises. Based on these features and on adversarial learning, we develop a novel structure restoration network to predict the deformation mapping in CT images before and after a fracture for the precise structural reconstruction of any fracture. Furthermore, a self-supervised strategy based on pelvic anatomical symmetry priors is developed to optimize the details of the restored pelvic structure. Finally, the restored pelvis is used as a template to generate a surgical reduction plan in which the fragments are repositioned in an efficient jigsaw puzzle registration manner. Extensive experiments on simulated and clinical datasets, including scans with metal artifacts, show that our method achieves good accuracy and robustness: a mean SSIM of 90.7% for restorations, with translational errors of 2.88 mm and rotational errors of 3.18°for reductions in real datasets. Our method takes 52.9 s to complete the surgical planning in the phantom study, representing a significant acceleration compared to standard clinical workflows. Our method may facilitate effective surgical planning for pelvic fractures tailored to individual patients in clinical settings.

Fragment distance-guided dual-stream learning for automatic pelvic fracture segmentation.

Pelvic fracture is a complex and severe injury. Accurate diagnosis and treatment planning require the segmentation of the pelvic structure and the fractured fragments from preoperative CT scans. However, this segmentation is a challenging task, as the fragments from a pelvic fracture typically exhibit considerable variability and irregularity in the morphologies, locations, and quantities. In this study, we propose a novel dual-stream learning framework for the automatic segmentation and category labeling of pelvic fractures. Our method uniquely identifies pelvic fracture fragments in various quantities and locations using a dual-branch architecture that leverages distance learning from bone fragments. Moreover, we develop a multi-size feature fusion module that adaptively aggregates features from diverse receptive fields tailored to targets of different sizes and shapes, thus boosting segmentation performance. Extensive experiments on three pelvic fracture datasets from different medical centers demonstrated the accuracy and generalizability of the proposed method. It achieves a mean Dice coefficient and mean Sensitivity of 0.935±0.068 and 0.929±0.058 in the dataset FracCLINIC, and 0.955±0.072 and 0.912±0.125 in the dataset FracSegData, which are superior than other comparing methods. Our method optimizes the process of pelvic fracture segmentation, potentially serving as an effective tool for preoperative planning in the clinical management of pelvic fractures.

Heart and great vessels segmentation in congenital heart disease via CNN and conditioned energy function postprocessing.

PURPOSE: The segmentation of the heart and great vessels in CT images of congenital heart disease (CHD) is critical for the clinical assessment of cardiac anomalies and the diagnosis of CHD. However, the diverse types and abnormalities inherent in CHD present significant challenges to comprehensive heart segmentation. METHODS: We proposed a novel two-stage segmentation approach, integrating a Convolutional Neural Network (CNN) with a postprocessing method with conditioned energy function for pulmonary and aorta. The initial stage employs a CNN enhanced by a gated self-attention mechanism for the segmentation of five primary heart structures and two major vessels. Subsequently, the second stage utilizes a conditioned energy function specifically tailored to refine the segmentation of the pulmonary artery and aorta, ensuring vascular continuity. RESULTS: Our method was evaluated on a public dataset including 110 3D CT volumes, encompassing 16 CHD variants. Compared to prevailing segmentation techniques (U-Net, V-Net, Unetr, dynUnet), our approach demonstrated improvements of 1.02, 1.04, and 1.41% in Dice Coefficient (DSC), Intersection over Union (IOU), and the 95th percentile Hausdorff Distance (HD95), respectively, for heart structure segmentation. For the two great vessels, the enhancements were 1.05, 1.07, and 1.42% in these metrics. CONCLUSION: The outcomes on the public dataset affirm the efficacy of our proposed segmentation method. Precise segmentation of the entire heart and great vessels can significantly aid in the diagnosis and treatment of CHD, underscoring the clinical relevance of our findings.

Adaptive Multi-Dimensional Weighted Network With Category-Aware Contrastive Learning for Fine-Grained Hand Bone Segmentation.

Accurately delineating and categorizing individual hand bones in 3D ultrasound (US) is a promising technology for precise digital diagnostic analysis. However, this is a challenging task due to the inherent imaging limitations of the US and the insignificant feature differences among numerous bones. In this study, we have proposed a novel deep learning-based solution for pediatric hand bone segmentation in the US. Our method is unique in that it allows for effective detailed feature mining through an adaptive multi-dimensional weighting attention mechanism. It innovatively implements a category-aware contrastive learning method to highlight inter-class semantic feature differences, thereby enhancing the category discrimination performance of the model. Extensive experiments on the challenging pediatric clinical hand 3D US datasets show the outstanding performance of the proposed method in segmenting thirty-eight bone structures, with the average Dice coefficient of 90.0%. The results outperform other state-of-the-art methods, demonstrating its effectiveness in fine-grained hand bone segmentation. Our method will be globally released as a plugin in the 3D Slicer, providing an innovative and reliable tool for relevant clinical applications.

Bone age assessment based on three-dimensional ultrasound and artificial intelligence compared with paediatrician-read radiographic bone age: protocol for a prospective, diagnostic accuracy study.

INTRODUCTION: Radiographic bone age (BA) assessment is widely used to evaluate children's growth disorders and predict their future height. Moreover, children are more sensitive and vulnerable to X-ray radiation exposure than adults. The purpose of this study is to develop a new, safer, radiation-free BA assessment method for children by using three-dimensional ultrasound (3D-US) and artificial intelligence (AI), and to test the diagnostic accuracy and reliability of this method. METHODS AND ANALYSIS: This is a prospective, observational study. All participants will be recruited through Paediatric Growth and Development Clinic. All participants will receive left hand 3D-US and X-ray examination at the Shanghai Sixth People's Hospital on the same day, all images will be recorded. These image related data will be collected and randomly divided into training set (80% of all) and test set (20% of all). The training set will be used to establish a cascade network of 3D-US skeletal image segmentation and BA prediction model to achieve end-to-end prediction of image to BA. The test set will be used to evaluate the accuracy of AI BA model of 3D-US. We have developed a new ultrasonic scanning device, which can be proposed to automatic 3D-US scanning of hands. AI algorithms, such as convolutional neural network, will be used to identify and segment the skeletal structures in the hand 3D-US images. We will achieve automatic segmentation of hand skeletal 3D-US images, establish BA prediction model of 3D-US, and test the accuracy of the prediction model. ETHICS AND DISSEMINATION: The Ethics Committee of Shanghai Sixth People's Hospital approved this study. The approval number is 2022-019. A written informed consent will be obtained from their parent or guardian of each participant. Final results will be published in peer-reviewed journals and presented at national and international conferences. TRIAL REGISTRATION NUMBER: ChiCTR2200057236.

Two-Stage Structure-Focused Contrastive Learning for Automatic Identification and Localization of Complex Pelvic Fractures.

Pelvic fracture is a severe trauma with a high rate of morbidity and mortality. Accurate and automatic diagnosis and surgical planning of pelvic fracture require effective identification and localization of the fracture zones. This is a challenging task due to the complexity of pelvic fractures, which often exhibit multiple fragments and sites, large fragment size differences, and irregular morphology. We have developed a novel two-stage method for the automatic identification and localization of complex pelvic fractures. Our method is unique in that it allows to combine the symmetry properties of the pelvic anatomy and capture the symmetric feature differences caused by the fracture on both the left and right sides, thereby overcoming the limitations of existing methods which consider only image or geometric features. It implements supervised contrastive learning with a novel Siamese deep neural network, which consists of two weight-shared branches with a structural attention mechanism, to minimize the confusion of local complex structures of the pelvic bones with the fracture zones. A structure-focused attention (SFA) module is designed to capture the spatial structural features and enhances the recognition ability of fracture zones. Comprehensive experiments on 103 clinical CT scans from the publicly available dataset CTPelvic1K show that our method achieves a mean accuracy and sensitivity of 0.92 and 0.93, which are superior to those reported with three SOTA contrastive learning methods and five advanced classification networks, demonstrating the effectiveness of identifying and localizing various types of complex pelvic fractures from clinical CT images.

A review on AI-based medical image computing in head and neck surgery.

Head and neck surgery is a fine surgical procedure with a complex anatomical space, difficult operation and high risk. Medical image computing (MIC) that enables accurate and reliable preoperative planning is often needed to reduce the operational difficulty of surgery and to improve patient survival. At present, artificial intelligence, especially deep learning, has become an intense focus of research in MIC. In this study, the application of deep learning-based MIC in head and neck surgery is reviewed. Relevant literature was retrieved on the Web of Science database from January 2015 to May 2022, and some papers were selected for review from mainstream journals and conferences, such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Physics in Medicine and Biology, Medical Physics, MICCAI, etc. Among them, 65 references are on automatic segmentation, 15 references on automatic landmark detection, and eight references on automatic registration. In the elaboration of the review, first, an overview of deep learning in MIC is presented. Then, the application of deep learning methods is systematically summarized according to the clinical needs, and generalized into segmentation, landmark detection and registration of head and neck medical images. In segmentation, it is mainly focused on the automatic segmentation of high-risk organs, head and neck tumors, skull structure and teeth, including the analysis of their advantages, differences and shortcomings. In landmark detection, the focus is mainly on the introduction of landmark detection in cephalometric and craniomaxillofacial images, and the analysis of their advantages and disadvantages. In registration, deep learning networks for multimodal image registration of the head and neck are presented. Finally, their shortcomings and future development directions are systematically discussed. The study aims to serve as a reference and guidance for researchers, engineers or doctors engaged in medical image analysis of head and neck surgery.

Probiotics for Preventing and Treating Small Intestinal Bacterial Overgrowth: A Meta-Analysis and Systematic Review of Current Evidence.

The present study conducted a meta-analysis and systematic review of current evidence to assess the efficacy of probiotics in preventing or treating small intestinal bacterial overgrowth (SIBO). Relevant studies from PubMed, Embase, and the Cochrane Central Register of Controlled Trials, until May 2016, were assimilated. The prevention efficacy was assessed by the incidence of SIBO in the probiotic group, and the treatment efficacy by the SIBO decontamination rate, reduction in H2 concentration, and symptom improvement. The relative risk (RR) and weighted mean difference (WMD) were used as effect measures and the random-effects model used for meta-analysis. A total of 14 full-text articles and 8 abstracts were included for the systematic review, and 18 studies were eligible for data synthesis. Patients on probiotic usage showed an insignificant trend toward low SIBO incidence [RR=0.54; 95% confidence intervals (CI), 0.19-1.52; P=0.24]. The pooled SIBO decontamination rate was 62.8% (51.5% to 72.8%). The probiotics group showed a significantly higher SIBO decontamination rate than the nonprobiotic group (RR=1.61; 95% CI, 1.19-2.17; P<0.05). Also, the H2 concentration was significantly reduced among probiotic users (WMD=-36.35 ppm; 95% CI, -44.23 to -28.47 ppm; P<0.05). Although probiotics produced a marked decrease in the abdominal pain scores (WMD=-1.17; 95% CI, -2.30 to -0.04; P<0.05), it did not significantly reduce the daily stool frequency (WMD=-0.09; 95% CI, -0.47 to 0.29). Therefore, the present findings indicated that probiotics supplementation could effectively decontaminate SIBO, decrease H2 concentration, and relieve abdominal pain, but were ineffective in preventing SIBO.

Endoscopic ultrasound-guided fine needle core biopsy for the diagnosis of pancreatic malignant lesions: a systematic review and Meta-Analysis.

Endoscopic ultrasound-guided fine needle core biopsy (EUS-FNB) has been used as an effective method of diagnosing pancreatic malignant lesions. It has the advantage of providing well preserved tissue for histologic grading and subsequent molecular biological analysis. In order to estimate the diagnostic accuracy of EUS-FNB for pancreatic malignant lesions, studies assessing EUS-FNB to diagnose solid pancreatic masses were selected via Medline. Sixteen articles published between 2005 and 2015, covering 828 patients, met the inclusion criteria. The summary estimates for EUS-FNB differentiating malignant from benign solid pancreatic masses were: sensitivity 0.84 (95% confidence interval (CI), 0.82-0.87); specificity 0.98 (95% CI, 0.93-1.00); positive likelihood ratio 8.0 (95% CI 4.5-14.4); negative likelihood ratio 0.17 (95% CI 0.10-0.26); and DOR 64 (95% CI 30.4-134.8). The area under the sROC curve was 0.96. Subgroup analysis did not identify other factors that could substantially affect the diagnostic accuracy, such as the study design, location of study, number of centers, location of lesion, whether or not a cytopathologist was present, and so on. EUS-FNB is a reliable diagnostic tool for solid pancreatic masses and should be especially considered for pathology where histologic morphology is preferred for diagnosis.

Diagnostic Accuracy of Serum Procalcitonin for Spontaneous Bacterial Peritonitis Due to End-stage Liver Disease: A Meta-analysis.

Early diagnosis and prompt treatment of spontaneous bacterial peritonitis (SBP) due to end-stage liver disease is vital to shorten hospital stays and reduce mortality. Many studies have explored the potential usefulness of serum procalcitonin (PCT) in predicting SBP. The aim of this study is to evaluate the overall diagnostic accuracy of PCT levels for identifying SBP due to end-stage liver disease.After performing a systematic search of the Medline, Embase, and Cochrane databases for studies that evaluated the diagnostic role of PCT for SBP, sensitivity, specificity, and other measures of accuracy of PCT concentrations in serum for SBP diagnosis were pooled using random-effects models. A summary receiver operating characteristic curve was used to summarize overall test performance.Seven publications met the inclusion criteria covering 742 episodes of suspected SBP along with 339 confirmed cases. The summary estimates for serum PCT in the diagnosis of SBP attributable to end-stage liver disease were: sensitivity 0.82 (95% CI 0.79-0.87), specificity 0.86 (95% CI 0.82-0.89), positive likelihood ratio 4.94 (95% CI 2.28-10.70), negative likelihood ratio 0.22 (95% CI 0.10-0.52), and diagnostic OR 22.55 (95% CI 7.01-108.30). The area under the curve was 0.92. There was evidence of significant heterogeneity but no evidence of publication bias.Serum PCT is a relatively sensitive and specific test for the identification of SBP. However, due to the limited high-quality studies available, medical decisions should be carefully made in the context of both PCT test results and other clinical findings.