OBJECTIVE: To elucidate how spinal manipulative therapy (SMT) exerts its analgesic effects through regulating brain function in lumbar disc herniation (LDH) patients by utilizing resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: From September 2021 to September 2023, we enrolled LDH patients (LDH group, n=31) and age- and sex-matched healthy controls (HCs, n=28). LDH group underwent rs-fMRI at 2 distinct time points (TPs): prior to the initiation of SMT (TP1) and subsequent to the completion of the SMT sessions (TP2). SMT was administered once every other day for 30 min per session, totally 14 treatment sessions over a span of 4 weeks. HCs did not receive SMT treatment and underwent only one fMRI scan. Additionally, participants in LDH group completed clinical questionnaires on pain using the Visual Analog Scale (VAS) and the Japanese Orthopedic Association (JOA) score, whereas HCs did not undergo clinical scale assessments. The effects on the brain were jointly characterized using the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo). Correlation analyses were conducted between specific brain regions and clinical scales. RESULTS: Following SMT treatment, pain symptoms in LDH patients were notably alleviated and accompanied by evident activation of effects in the brain. In comparison to TP1, TP2 exhibited the most significant increase in ALFF values for Temporal_Sup_R and the most notable decrease in ALFF values for Paracentral_Lobule_L (voxelwise P<0.005; clusters >30; FDR correction). Additionally, the most substantial enhancement in ReHo values was observed for the Cuneus_R, while the most prominent reduction was noted for the Olfactory_R (voxelwise P<0.005; clusters >30; FDR correction). Moreover, a comparative analysis revealed that, in contrast to HCs, LDH patients at TP1 exhibited the most significant increase in ALFF values for Temporal_Pole_Sup_L and the most notable decrease in ALFF values for Frontal_Mid_L (voxelwise P<0.005; clusters >30; FDR correction). Furthermore, the most significant enhancement in ReHo values was observed for Postcentral_L, while the most prominent reduction was identified for ParaHippocampal_L (voxelwise P<0.005; clusters >30; FDR correction). Notably, correlation analysis with clinical scales revealed a robust positive correlation between the Cuneus_R score and the rate of change in the VAS score (r=0.9333, P<0.0001). CONCLUSIONS Long-term chronic lower back pain in patients with LDH manifests significant activation of the "AUN-DMN-S1-SAN" neural circuitry. The visual network, represented by the Cuneus_R, is highly likely to be a key brain network in which the analgesic efficacy of SMT becomes effective in treating LDH patients. (Trial registration No. NCT06277739).
Humans ; Magnetic Resonance Imaging ; Intervertebral Disc Displacement/diagnostic imaging* ; Male ; Female ; Brain/diagnostic imaging* ; Adult ; Manipulation, Spinal/methods* ; Middle Aged ; Lumbar Vertebrae/physiopathology* ; Pain Management ; Rest ; Case-Control Studies

OBJECTIVES: According to the World Health Organization (WHO), infertility rates have been steadily rising worldwide. Identifying risk factors for contrast agent reflux into the myometrium during hysterosalpingo-contrast sonography (HyCoSy) is of clinical significance in reducing this complication and improving infertility treatment. However, there is currently no standardized pre-evaluation method for predicting intra-myometrial contrast reflux, with clinical assessment often relying on physician experience and patient symptoms. This study aims to identify imaging risk factors for contrast agent reflux into the myometrium using four-dimensional (4D) HyCoSy and to construct a nomogram-based predictive model to assist in clinical decision-making. METHODS: A retrospective analysis was conducted on 1 274 infertile women who underwent 4D HyCoSy at the Women and Children's Hospital of Hunan and the the Third Xiangya Hospital of Central South University from January 1, 2020, to December 15, 2022. Patients were divided into a reflux group (n=234) and a non-reflux group (n=1 040) based on the presence of intra-myometrial contrast reflux. Univariate and multivariable Logistic regression analyses were used to identify significant predictors, which were then used to construct a nomogram model. Internal validation was performed using 500 bootstrap resamples. RESULTS: The age of the reflux group was significantly higher than that of the non-reflux group [(31.82±5.27) years vs (30.66±4.83) years, P=0.001 1]. Primary infertility was more common in the non-reflux group (50.96%), while secondary infertility dominated in the reflux group (76.50%), with 72.65% having a history of gynecological surgery (P<0.001). Abnormal menstrual volume and discomfort during the procedure were more common in the reflux group, while the non-reflux group tolerated higher contrast agent doses (P<0.001). Imaging differences included endometrial thickness, tubal wall smoothness, and peritoneal contrast dispersion, with the non-reflux group showing thicker endometrium and smoother, more patent tubes. The nomogram model yielded an area under the curve (AUC) of 0.854, indicating good predictive performance. The AUC of the decision curve analysis (DCA) for internal validation of the model was 0.737. When the threshold probability for contrast agent reflux into the myometrium ranged from 0.05 to 0.95, the maximum net benefit reached 0.18. The net benefit of applying the nomogram predictive model exceeded that of either full intervention or no intervention, indicating that the model demonstrates good clinical predictive performance. CONCLUSIONS The nomogram model, based on infertility type, endometrial thickness, contrast agent dose, and discomfort symptoms, effectively predicts intra-myometrial contrast agent reflux after 4D HyCoSy. It provides a valuable tool for clinicians to implement early preventive measures and reduce the risk of contrast leakage and associated complications.
Humans ; Female ; Nomograms ; Contrast Media/adverse effects* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Hysterosalpingography/methods* ; Infertility, Female/diagnostic imaging* ; Myometrium/diagnostic imaging* ; Risk Factors

OBJECTIVES: In recent years, the incidence and detection rate of pancreatic cystic lesions (PCLs) have increased significantly. Endoscopic ultrasound (EUS) plays an indispensable role in the diagnosis and differential diagnosis of PCLs. However, evidence comparing the diagnostic performance of EUS-guided fine-needle aspiration (EUS-FNA) and fine-needle biopsy (FNB) remains limited. This study aims to compare the diagnostic yield, adequacy of tissue acquisition, and safety between EUS-FNA and EUS-FNB in evaluating PCLs to inform clinical practice. METHODS: A retrospective review was conducted on patients with PCLs who underwent either EUS-FNA or EUS-FNB between January 2014 and August 2021. The diagnostic yield, tissue acquisition adequacy, and incidence of adverse events were compared between the 2 groups. RESULTS: A total of 90 patients with PCLs were included (52 in the FNA group and 38 in the FNB group). The diagnostic yield was similar between the FNA and FNB groups (94.2% vs 94.7%, P>0.05). The adequacy of tissue acquisition was 71.2% in the FNA group and 81.6% in the FNB group (P>0.05). No statistically significant difference was observed in the incidence of adverse events between the 2 groups (P>0.05). CONCLUSIONS Both EUS-FNA and EUS-FNB demonstrate equally high diagnostic yields and tissue adequacy in PCLs, with excellent safety profiles. Both methods are safe and effective diagnostic tools for evaluating PCLs.
Humans ; Endoscopic Ultrasound-Guided Fine Needle Aspiration/adverse effects* ; Retrospective Studies ; Female ; Male ; Pancreatic Cyst/diagnostic imaging* ; Middle Aged ; Biopsy, Fine-Needle/adverse effects* ; Aged ; Pancreatic Neoplasms/diagnosis* ; Adult ; Endosonography/methods* ; Pancreas/pathology* ; Diagnosis, Differential

OBJECTIVES: Renal cell carcinoma (RCC) is a malignant renal tumor that poses a significant threat to patient health. Accurate preoperative pathological grading plays a crucial role in determining the appropriate treatment for this disease. Currently, deep learning technology has become an important method for pathological grading of RCC. However, existing methods primarily rely on single-phase computed tomography (CT) imaging for analysis and prediction, which has limitations such as missing small lesions, one-sided evaluation, and local focusing issues. Therefore, this study proposes a multi-modal deep learning algorithm that integrates multi-phase enhanced CT images with clinical variable data, aiming to provide a basis for predicting the pathological grading of RCC. METHODS: First, the algorithm took four-phase enhanced CT images from the plain scan, arterial phase, venous phase, and delayed phase, along with clinical variables, as inputs. Then, an embedding encoding module was used to extract heterogeneous information from the clinical variables, and a 3-dimensional (3D) ResNet50 model was employed to capture spatial information from the multi-phase enhanced CT image data. Finally, a Fusion module deeply integrated the feature information from clinical variables and each phase's CT image features, further utilizing a cross-self-attention mechanism to achieve multi-phase feature fusion. This approach comprehensively captures the deep semantic information from the patient data, fully leveraging the complementary advantages of multi-modal and multi-phase data. To validate the effectiveness of the proposed method, a total of 1 229 RCC patients were approved by ethics review were included to train the model. RESULTS: Experimental results demonstrated superior performance compared to traditional radiomics and state-of-the-art deep learning methods, achieving an accuracy of 83.87%, a recall rate of 95.04%, and an F1-score of 82.23%. CONCLUSIONS The proposed algorithm exhibits strong stability and sensitivity, significantly enhancing the predictive performance of RCC pathological grading. It offers a novel approach for accurate RCC diagnosis and personalized treatment planning.
Humans ; Carcinoma, Renal Cell/pathology* ; Deep Learning ; Kidney Neoplasms/diagnostic imaging* ; Tomography, X-Ray Computed/methods* ; Algorithms ; Neoplasm Grading ; Male ; Female ; Middle Aged

OBJECTIVES: The diagnostic value of ultrasonographic quantitative indicators of umbilical cord coiling, such as the umbilical coiling index (UCI) and pitch value, in identifying hypercoiling and predicting adverse pregnancy outcomes remains controversial. This study aims to evaluate the predictive value of UCI, pitch value, and the cerebroplacental ratio in pregnancies complicated by umbilical cord hypercoiling. METHODS: Pregnant women with densely coiled umbilical cords identified by routine obstetric ultrasound at Changsha Maternal and Child Health Hospital between November 2022 and November 2024 were enrolled. Complete clinical data, including UCI, pitch value, and cerebroplacental ratio (CPR), were collected. Pregnancy outcome scores were calculated, and newborns were categorized into the normal outcome group (n=177) and adverse outcome group (n=85), with the latter further subdivided into mild (n=51), moderate (n=19), and severe (n=15) subgroups. Differences in baseline data, UCI, pitch value, and incidence of CRP<1 were compared between groups and among subgroups. Correlations between UCI, pitch value, and adverse pregnancy outcomes were analyzed. Receiver operating characteristic (ROC) curve were used to assess the predictive performance of UCI, pitch value, CPR<1, and their combinations. RESULTS: Compared with the normal outcome group, the adverse outcome group had higher age, parity, parity, incidence of CPR<1, and UCI, while gestational age at delivery and pitch values were lower (all P<0.05). The incidence of obesity, gestational diabetes mellitus, and hypertensive disorders of pregnancy did not differ significantly between the 2 groups (all P>0.05). The normal outcome group showed lower UCI and higher pitch values than all 3 adverse outcome subgroups (all P<0.05), while differences among the 3 adverse subgroups were not significant (all P>0.05). UCI was positively correlated with adverse pregnancy outcomes (r_s=0.350, P<0.05), whereas pitch value was negatively correlated (r_s=-0.286, P<0.05). ROC curve analysis showed that the area under the curve (AUC) values for predicting adverse outcomes were 0.837 for UCI, 0.886 for pitch value, and 0.610 for CPR<1, with sensitivities of 77.6%, 82.4%, and 27.1% and specificities of 78.5%, 83.6%, and 94.9%, respectively. The combined UCI+CPR<1 and pitch value+CPR<1 models yielded AUCs of 0.841 and 0.886, with sensitivities of 78.8% and 81.2% and specificities of 78.5% and 84.2%, respectively. No significant differences were found between the AUCs of UCI and pitch value (P>0.05), but both outperformed CPR<1 alone (both P<0.001). The combined models showed no significant improvement over UCI or pitch value alone (both P>0.05), though both were superior to CPR<1 alone (both P<0.001). CONCLUSIONS Most umbilical cord hypercoiling cases had favorable outcomes, with UCI, pitch value, CPR<1 and their combinations demonstrating significant predictive value for adverse pregnancy outcomes.
Humans ; Female ; Pregnancy ; Pregnancy Outcome ; Adult ; Ultrasonography, Prenatal/methods* ; Umbilical Cord/diagnostic imaging* ; Hemodynamics ; Predictive Value of Tests ; Infant, Newborn ; ROC Curve

Objective:To exploring the value of MR neuroimaging for quantitative assessment of the facial nerve and peripheral lymph nodes in patients with acute peripheral facial paralysis. Methods:Based on a prospective experimental design, 32 patients with idiopathic peripheral facial palsy were enrolled in the experiment. Based on MR neuroimaging technology, MR high-resolution thin-layer images of bilateral facial nerves were acquired. The diameters of different segments of the bilateral facial nerve were measured, including the labyrinthine segment, the geniculate ganglion, the horizontal segment, the vertical segment, the stem-mammary foramen segment, the trunk of the parotid segment, the temporal trunk, and the cervical trunk, as well as the quantitative indicators of peri-auricular and parotid lymph nodes（number, length and diameter of the largest lymph nodes）. Differences in quantitative indices of nerve diameter and peripheral lymph nodes between the paraplegic and healthy sides were compared using the paired t-test and Wilcoxon signed rank test. Results:The diameter of geniculate ganglion, mastoid foramen stem, parotid main trunk, temporal facial trunk, and cervical facial trunk were notably increased on the facial paralysis side compared to the contralateral side（P<0.05）. However, no significant differences were observed in the diameter of labyrinthine segment, horizontal segment, or vertical segment compared to the contralateral side. There were significantly more periauricular lymph nodes on the facial paralysis side than the contralateral side（P=0.001）. Conclusion:MR neuroimaging enables the quantitative assessment of structural changes in the facial nerve of patients with acute peripheral facial paralysis, demonstrating nerve enlargement in the geniculate ganglion, stylomastoid foramen segment, main trunk of the parotid segment, temporal facial trunk, and cervical facial trunk. Additionally, an increased number of periauricular lymph nodes is observed on the affected side. These findings may aid clinicians in assessing the efficacy of treatments and predict the prognosis of these patients.
Humans ; Facial Nerve/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Prospective Studies ; Female ; Male ; Neuroimaging/methods* ; Lymph Nodes/diagnostic imaging* ; Facial Paralysis/diagnostic imaging* ; Adult ; Middle Aged

Objective:To explore the value of high resolution computed tomography（HRCT） combined with Magnetic Resonance Imaging（MRI） in the diagnosis of inner ear malformation. Methods:HRCT and MRI data of 82 patients with inner ear malformations were analyzed retrospectively. HRCT MPR and CPR reconstruction of the inner ear structure, facial nerve canal and oblique sagittal MRI reconstruction of the internal auditory canal were performed. The inner ear malformations were classified, the conditions of facial nerve canal and cochlear nerve were evaluated. The association between inner ear malformation and cochlear nerve dysplasia were analyzed by Chi-square test with continuity correction. Results:Among the 82 patients with inner ear malformations,there were 49 cases of bilateral symmetry, 11 cases of bilateral asymmetry and 22 cases of unilateral inner ear malformations. Respectively, the most prevalent types were IP-Ⅱ（42.96%）, dilatation of atrium aqueduct（18.31%） and malformations of atrium and semicircular canal 19.72%. Out of 50 cases of cochlear malformations,only 3 were isolated cochlear malformations, and the rest were accompanied by other malformations of varying degrees. In the 67 ears examined by MRI, 26（38.81%） had cochlear nerve deficiency（CND）, and the incidence of CND varied with different types of inner ear malformations. Out of 142 ears, 28（19.72%） had abnormalities of the facial nerve canal. Conclusion:HRCT combined with MRI can accurately distinguish the types of inner ear malformation and effectively evaluate the facial nerve canal and cochlear nerve, and further provides the important finger and Guide value for the clinician to formulate the reasonable treatment and the operation plan.
Humans ; Ear, Inner/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Retrospective Studies ; Female ; Male ; Tomography, X-Ray Computed/methods* ; Child ; Adolescent ; Adult ; Child, Preschool ; Cochlear Nerve/diagnostic imaging* ; Facial Nerve/abnormalities* ; Cochlea/abnormalities* ; Infant ; Young Adult

INTRODUCTION: In this study, we aimed to compare the performance of a convolutional neural network (CNN)-based deep learning model that was trained on a dataset of normal and abnormal paediatric elbow radiographs with that of paediatric emergency department (ED) physicians on a binomial classification task. METHODS: A total of 1,314 paediatric elbow lateral radiographs (patient mean age 8.2 years) were retrospectively retrieved and classified based on annotation as normal or abnormal (with pathology). They were then randomly partitioned to a development set (993 images); first and second tuning (validation) sets (109 and 100 images, respectively); and a test set (112 images). An artificial intelligence (AI) model was trained on the development set using the EfficientNet B1 network architecture. Its performance on the test set was compared to that of five physicians (inter-rater agreement: fair). Performance of the AI model and the physician group was tested using McNemar test. RESULTS: The accuracy of the AI model on the test set was 80.4% (95% confidence interval [CI] 71.8%-87.3%), and the area under the receiver operating characteristic curve (AUROC) was 0.872 (95% CI 0.831-0.947). The performance of the AI model vs. the physician group on the test set was: sensitivity 79.0% (95% CI: 68.4%-89.5%) vs. 64.9% (95% CI: 52.5%-77.3%; P = 0.088); and specificity 81.8% (95% CI: 71.6%-92.0%) vs. 87.3% (95% CI: 78.5%-96.1%; P = 0.439). CONCLUSION The AI model showed good AUROC values and higher sensitivity, with the P-value at nominal significance when compared to the clinician group.
Humans ; Deep Learning ; Child ; Retrospective Studies ; Male ; Female ; Radiography/methods* ; ROC Curve ; Elbow/diagnostic imaging* ; Neural Networks, Computer ; Child, Preschool ; Elbow Joint/diagnostic imaging* ; Emergency Service, Hospital ; Adolescent ; Infant ; Artificial Intelligence

OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult

INTRODUCTION: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc., Seoul, Republic of Korea) segmentation software in a Southeast Asian community-based cohort with normal cognition, mild cognitive impairment (MCI) and dementia. METHOD: Study participants belonged to the community-based Biomarker and Cognition Study in Singapore. Participants aged between 30 and 95 years, having cognitive concerns, with no diagnosis of major psychiatric, neurological or systemic disorders who were recruited consecutively between April 2022 and July 2023 were included. Participants underwent neuropsychological assessments and structural MRI, and were classified as cognitively normal, with MCI or with dementia. MRI pre-processing using automated pipelines, along with human-based visual ratings, were compared against AI-based automated AQUA output. Default mode network grey matter (GM) volumes were compared between cognitively normal, MCI and dementia groups. RESULTS: A total of 90 participants (mean age at visit was 63.32±10.96 years) were included in the study (30 cognitively normal, 40 MCI and 20 dementia). Non-parametric Spearman correlation analysis indicated that AQUA-based and human-based visual ratings were correlated with total (ρ=0.66; P<0.0001), periventricular (ρ=0.50; P<0.0001) and deep (ρ=0.57; P<0.0001) white matter hyperintensities (WMH). Additionally, volumetric WMH obtained from AQUA and automated pipelines was also strongly correlated (ρ=0.84; P<0.0001) and these correlations remained after controlling for age at visit, sex and diagnosis. Linear regression analyses illustrated significantly different AQUA-derived default mode network GM volumes between cognitively normal, MCI and dementia groups. Dementia participants had significant atrophy in the posterior cingulate cortex compared to cognitively normal participants (P=0.021; 95% confidence interval [CI] -1.25 to -0.08) and in the hippocampus compared to cognitively normal (P=0.0049; 95% CI -1.05 to -0.16) and MCI participants (P=0.0036; 95% CI -1.02 to -0.17). CONCLUSION Our findings demonstrate high concordance between human-based visual ratings and AQUA-based ratings of WMH. Additionally, the AQUA GM segmentation pipeline showed good differentiation in key regions between cognitively normal, MCI and dementia participants. Based on these findings, the automated AQUA software could aid clinicians in examining MRI scans of patients with cognitive impairment.
Humans ; Cognitive Dysfunction/pathology* ; Magnetic Resonance Imaging/methods* ; Male ; Middle Aged ; Female ; Aged ; Artificial Intelligence ; Software ; Dementia/diagnostic imaging* ; Aged, 80 and over ; Adult ; Singapore ; Neuropsychological Tests ; Brain/pathology* ; Cohort Studies ; Gray Matter/pathology* ; Southeast Asian People