Enhancing RNA-lipid nanoparticle delivery: Organ- and cell-specificity and barcoding strategies.

Recent advancements in RNA therapeutics highlight the critical need for precision gene delivery systems that target specific organs and cells. Lipid nanoparticles (LNPs) have emerged as key vectors in delivering mRNA and siRNA, offering protection against enzymatic degradation, enabling targeted delivery and cellular uptake, and facilitating RNA cargo release into the cytosol. This review discusses the development and optimization of organ- and cell-specific LNPs, focusing on their design, mechanisms of action, and therapeutic applications. We explore innovations such as DNA/RNA barcoding, which facilitates high-throughput screening and precise adjustments in formulations. We address major challenges, including improving endosomal escape, minimizing off-target effects, and enhancing delivery efficiencies. Notable clinical trials and recent FDA approvals illustrate the practical applications and future potential of LNP-based RNA therapies. Our findings suggest that while considerable progress has been made, continued research is essential to resolve existing limitations and bridge the gap between preclinical and clinical evaluation of the safety and efficacy of RNA therapeutics. This review highlights the dynamic progress in LNP research. It outlines a roadmap for future advancements in RNA-based precision medicine.

Uncovering the Role of Anoikis-Related Genes in Modulating Immune Infiltration and Pathogenesis of Diabetic Kidney Disease.

Background: Diabetic kidney disease (DKD) is an intricate complication of diabetes with limited treatment options. Anoikis, a programmed cell death activated by loss of cell anchorage to the extracellular matrix, participated in various physiological and pathological processes. Our study aimed to elucidate the role of anoikis-related genes in DKD pathogenesis. Methods: Differentially expressed genes (DEGs) associated with anoikis in DKD were identified. Weighted gene co-expression network analysis (WGCNA) was conducted to identify DKD-correlated modules and assess their functional implications. A diagnostic model for DKD was developed using LASSO regression and Gene set variation analysis (GSVA) was performed for enrichment analysis. Experimental validation was employed to validate the significance of selected genes in the progression of DKD. Results: We identified 10 anoikis-related DEGs involved in key signaling pathways impacting DKD progression. WGCNA highlighted the yellow module's significant enrichment in immune response and regulatory pathways. Correlation analysis further revealed the association between immune infiltration and anoikis-related DEGs. Our LASSO regression-based diagnostic model demonstrated a well-predictive efficacy with seven identified genes. GSVA indicated that gene function in the high-risk group was primarily associated with immune regulation. Further experimental validation using diabetic mouse models and data analysis in the single-cell dataset confirmed the significance of PYCARD and SFN in DKD progression. High glucose stimulation in RAW264.7 and TCMK-1 cells showed significantly increased expression levels of both Pycard and Sfn. Co-expression analysis demonstrated distinct functions of PYCARD and SFN, with KEGG pathway analysis showing significant enrichment in immune regulation and cell proliferation pathway. Conclusion: In conclusion, our study provides valuable insights into the molecular mechanisms involved in DKD pathogenesis, specifically highlighting the role of anoikis-related genes in modulating immune infiltration. These findings suggest that targeting these genes may hold promise for future diagnostic and therapeutic approaches in DKD management.

Integrated 16S rRNA sequencing and metagenomics insights into microbial dysbiosis and distinct virulence factors in inflammatory bowel disease.

Introduction: The escalation of urbanization correlates with rising rates of inflammatory bowel disease (IBD), necessitating research into new etiological factors. This study aims to elucidate the gut microbiota profiles in IBD patients and compare them with healthy controls in a western city of China. Methods: We conducted a multicenter case-control study from the end of 2020, using 16S rRNA gene sequencing (n = 36) and metagenomic sequencing (n = 12) to analyze the gut microbiota of newly diagnosed IBD patients, including those with Crohn's disease (CD) and ulcerative colitis (UC). Results: Our results demonstrated a significant enrichment of the phylum Proteobacteria, particularly the genus Escherichia-Shigella, in CD patients. Conversely, the genus Enterococcus was markedly increased in UC patients. The core gut microbiota, such as the Christensenellaceae R-7 group, Fusicatenibacter, and Holdemanella, were primarily identified in healthy subjects. Additionally, significant interactions between the microbiome and virulence factors were observed. Discussion: The findings suggest that oxidative stress may play a pivotal role in the pathology of IBD. This study contributes to the growing dialogue about the impact of gut microbiota on the development of IBD and its variations across different geographies, highlighting potential avenues for further research.

DMAF-Net: deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs.

OBJECTIVES: Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the You Only Look Once (YOLO) network and verified the feasibility of this new method in aiding the detection of dental problems. METHODS: We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect 5 types of dental situations (impacted teeth, missing teeth, implants, crown restorations, and root canal-treated teeth) in panoramic radiography by improving the YOLO network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptively spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria. RESULTS: About 1474 panoramic radiographs were divided into training, validation, and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP[0.5:0.95]) are 91.8% and 63.7%, respectively. CONCLUSIONS: The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching, and clinical applications in the future.

Superficial white matter microstructural imaging method based on time-space fractional-order diffusion.

Objective. Microstructure imaging based on diffusion magnetic resonance signal is an advanced imaging technique that enablesin vivomapping of the brain's microstructure. Superficial white matter (SWM) plays an important role in brain development, maturation, and aging, while fewer microstructure imaging methods address the SWM due to its complexity. Therefore, this study aims to develop a diffusion propagation model to investigate the microstructural characteristics of the SWM region.Approach. In this paper, we hypothesize that the effect of cell membrane permeability and the water exchange between soma and dendrites cannot be neglected for typical clinical diffusion times (20 ms

Q-space imaging based on Gaussian radial basis function with Laplace regularization.

PURPOSE: To introduce the diffusion signal characteristics presented by spherical harmonics (SH) basis into the q-space imaging method based on Gaussian radial basis function (GRBF) to robustly reconstruct ensemble average diffusion propagator (EAP) in diffusion MRI (dMRI). METHODS: We introduced the Laplacian regularization of the signal into the dMRI imaging method based on GRBF, and derived the relevant indicators of microstructure imaging and the orientation distribution function (ODF) providing fiber bundle direction information based on EAP. In addition, this method is combined with a multi-compartment model to calculate the diameter of fiber bundle axons. The evaluation of the results included qualitative comparisons and quantitative assessments of the signal fitting. RESULTS: The results show that the proposed method achieves the more significant accuracy improvement in reconstructing signal. Meanwhile, ODFs estimated by the proposed method show the sharper profiles and less spurious peaks, even under the sparse and noisy conditions. In the 36 sets of axon diameter estimation experiments, 34 and 30 sets of results showed that the proposed method reduced the mean and SD of axon diameter estimates, respectively. Moreover, compared with the current state-of-the-art method, the mean and SD of axon diameter estimated by the proposed method are mostly lower, with 32 and 29 of 36 groups. CONCLUSION: The proposed method outperforms the GRBF regarding signal fitting and the estimation of the EAP and ODF with multi-shell sparse samples. Moreover, it shows the potential to recover important features of microstructures with less uncertainty by using proposed method together with multi-compartment models.

Brain fiber structure estimation based on principal component analysis and RINLM filter.

Diffusion magnetic resonance imaging is a technique for non-invasive detection of microstructure in the white matter of the human brain, which is widely used in neuroscience research of the brain. However, diffusion-weighted images(DWI) are sensitive to noise, which affects the subsequent reconstruction of fiber orientation direction, microstructural parameter estimation and fiber tracking. In order to better eliminate the noise in diffusion-weighted images, this study proposes a noise reduction method combining Marchenko-Pastur principal component analysis(MPPCA) and rotation-invariant non-local means filter(RINLM) to further remove residual noise and preserve the image texture detail information. In this study, the algorithm is applied to the fiber structure and the prevailing microstructural models within the human brain voxels based on simulated and real human brain datasets. Experimental comparisons between the proposed method and the state-of-the-art methods are performed in single-fiber, multi-fiber, crossed and curved-fiber regions as well as in different microstructure estimation models. Results demonstrated the superior performance of the proposed method in denoising DWI data, which can reduce the angular error in fiber orientation reconstruction to obtain more valid fiber structure estimation and enable more complete fiber tracking trajectories with higher coverage. Meanwhile, the method reduces the estimation errors of various white matter microstructural parameters and verifies the performance of the method in white matter microstructure estimation.

PAPNet: Convolutional network for pancreatic cyst segmentation.

BACKGROUND: Automatic segmentation of the pancreas and its tumor region is a prerequisite for computer-aided diagnosis. OBJECTIVE: In this study, we focus on the segmentation of pancreatic cysts in abdominal computed tomography (CT) scan, which is challenging and has the clinical auxiliary diagnostic significance due to the variability of location and shape of pancreatic cysts. METHODS: We propose a convolutional neural network architecture for segmentation of pancreatic cysts, which is called pyramid attention and pooling on convolutional neural network (PAPNet). In PAPNet, we propose a new atrous pyramid attention module to extract high-level features at different scales, and a spatial pyramid pooling module to fuse contextual spatial information, which effectively improves the segmentation performance. RESULTS: The model was trained and tested using 1,346 CT slice images obtained from 107 patients with the pathologically confirmed pancreatic cancer. The mean dice similarity coefficient (DSC) and mean Jaccard index (JI) achieved using the 5-fold cross-validation method are 84.53% and 75.81%, respectively. CONCLUSIONS: The experimental results demonstrate that the proposed new method in this study enables to achieve effective results of pancreatic cyst segmentation.

Effects of pressure on microstructure evolution of liquid Fe-S-Bi alloy during rapid solidification: A molecular dynamics study.

To understand the effects of pressure on microstructural evolution, a molecular dynamics simulation study has been performed under pressures of 0-20 GPa for liquid Fe-S-Bi alloy during the solidification process. The variations in the radial distribution function, average atomic energy, and H-A bond index of the cooling system are analyzed. The rapid solidification process of liquid Fe-S-Bi alloy into crystalline and amorphous alloys is investigated from different perspective. The results show that the glass transition temperature Tg, the sizes of the MnS atomic groups, and major bond-types increase almost linearly with increasing pressure. In addition, the recovery rate of Bi increased first and then decreased with increasing pressure, reaching a peak of 68.97% under 5 GPa. The manganese sulfide compound is embedded in the alloy with a spindle-shape under 20 GPa, which is a better clusters structure.

A predictive model for early therapeutic efficacy of colorectal liver metastases using multimodal MRI data.

BACKGROUND: Liver metastases is a pivotal factor of death in patients with colorectal cancer. The longitudinal data of colorectal liver metastases (CRLM) during treatment can monitor and reflect treatment efficacy and outcomes. OBJECTIVE: The objective of this study is to establish a radiomic model based on longitudinal magnetic resonance imaging (MRI) to predict chemotherapy response in patients with CRLM. METHODS: This study retrospectively enrolled longitudinal MRI data of five modalities on 100 patients. According to Response Evaluation Criteria in Solid Tumors (RECIST 1.1), 42 and 58 patients were identified as responders and non-responders, respectively. First, radiomic features were computed from different modalities of image data acquired pre-treatment and early-treatment, as well as their differences (Δ). Next, the features were screened by a two-sample t-test, max-relevance and min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO). Then, several ensemble radiomic models that integrate support vector machine (SVM), k-nearest neighbor (KNN), gradient boost decision tree (GBDT) and multi-layer perceptron (MLP) were established based on voting method to predict chemotherapy response. Data samples were divided into training and verification queues using a ratio of 8:2. Finally, we used the area under ROC curve (AUC) to evaluate model performance. RESULTS: Using the ensemble model developed using featue differences (Δ) computed from the longitudinal apparent diffusion coefficient (ADC) images, AUC is 0.9007±0.0436 for the training cohort. Applying to the testing cohort, AUC is 0.8958 and overall accuracy is 0.9. CONCLUSIONS: Study results demonstrate advantages and high performance of the ensemble radiomic model based on the radiomics feature difference of the longitudinal ADC images in predicting chemotherapy response, which has potential to assist treatment decision-making and improve clinical outcome.

Fully Connected Multi-Kernel Convolutional Neural Network Based on Alzheimer's Disease Diagnosis.

BACKGROUND: There is a shortage of clinicians with sufficient expertise in the diagnosis of Alzheimer's disease (AD), and cerebrospinal fluid biometric collection and positron emission tomography diagnosis are invasive. Therefore, it is of potential significance to obtain high-precision automatic diagnosis results from diffusion tensor imaging (DTI) through deep learning, and simultaneously output feature probability maps to provide clinical auxiliary diagnosis. OBJECTIVE: We proposed a factorization machine combined neural network (FMCNN) model combining a multi-function convolutional neural network (MCNN) with a fully convolutional network (FCN), while accurately diagnosing AD and mild cognitive impairment (MCI); corresponding fiber bundle visualization results are generated to describe their status. METHODS: First, the DTI data is preprocessed to eliminate the influence of external factors. The fiber bundles of the corpus callosum (CC), cingulum (CG), uncinate fasciculus (UNC), and white matter (WM) were then tracked based on deterministic fiber tracking. Then the streamlines are input into CNN, MCNN, and FMCNN as one-dimensional features for classification, and the models are evaluated by performance evaluation indicators. Finally, the fiber risk probability map is output through FMCNN. RESULTS: After comparing the model performance indicators of CNN, MCNN, and FMCNN, it was found that FMCNN showed the best performance in the indicators of accuracy, specificity, sensitivity, and area under the curve. By inputting the fiber bundles of the 10 regions of interest (UNC_L, UNC_R, UNC, CC, CG, CG+UNC, CG+CC, CC+UNC, CG+CC+UNC, and WM into CNN, MCNN, and FMCNN, respectively), WM shows the highest accuracy in CNN, MCNN, and FMCNN, which are 88.41%, 92.07%, and 96.95%, respectively. CONCLUSION: The FMCNN proposed here can accurately diagnose AD and MCI, and the generated fiber probability map can represent the risk status of AD and MCI.

CT classification model of pancreatic serous cystic neoplasm and mucinous cystic neoplasm based on deep transfer learning.

BACKGROUND: Pancreatic cancer is a highly lethal disease. The preoperative distinction between pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) remains a clinical challenge. OBJECTIVE: The goal of this study is to provide clinicians with supportive advice and avoid overtreatment by constructing a convolutional neural network (CNN) classifier to automatically identify pancreatic cancer using computed tomography (CT) images. METHODS: We construct a CNN model using a dataset of 6,173 CT images obtained from 107 pathologically confirmed pancreatic cancer patients at Shanghai Changhai Hospital from January 2017 to February 2022. We divide CT slices into three categories namely, SCN, MCN, and no tumor, to train the DenseNet201-based CNN model with multi-head spatial attention mechanism (MSAM-DenseNet201). The attention module enhances the network's attention to local features and effectively improves the network performance. The trained model is applied to process all CT image slices and finally realize the two categories classification of MCN and SCN patients through a joint voting strategy. RESULTS: Using a 10-fold cross validation method, this new MSAM-DenseNet201 model achieves a classification accuracy of 92.52%, a precision of 92.16%, a sensitivity of 92.16%, and a specificity of 92.86%, respectively. CONCLUSIONS: This study demonstrates the feasibility of using a deep learning network or classification model to help diagnose MCN and SCN cases. This, the new method has great potential for developing new computer-aided diagnosis systems and applying in future clinical practice.

Effects of MnS inclusions on mechanical behavior and damage mechanism of free-cutting steel: A molecular dynamics study.

In order to research the effect pattern of MnS inclusions on free-cutting steel, we study the microstructure evolution, the damage mechanism and the mechanical properties in free-cutting steel in the presence of MnS inclusions. Spindle shaped MnS is added as inclusions within the free-cutting steel. The mechanical properties were found to change when inclusions were present. The gained results show that the formation of voids causing fracture starts from the interface inside the matrix close to the MnS. From the point of view of nanocomposite strength, the main effect of MnS inclusions is related to stress concentration, leading to the effect of increased stresses near the interface between the interior of the matrix and the inclusions. The inclusions have lower Young's modulus and lower dislocation activity, resulting in smaller deformation of the alloy system, larger interfacial stress concentrations and earlier hole formation. The maximum strain and stress regions of the alloy also appear near the MnS inclusions, which leads to the formation of defects near the MnS inclusions and then fracture of the alloy. MnS inclusions adversely affect the tensile properties of the alloy, such as Young's modulus, yield stress and yield strain. By comparing the stress-strain curves of single crystal iron and alloy containing MnS inclusions, it is indicated that the yield strength of the latter decreases. Slip bands and dislocation lines are first generated around the MnS inclusion, and the phase transition is induced from the original single BCC structure to FCC, HCP and amorphous structures, and the atoms of FCC, HCP and amorphous structures increase with increasing strain, while those of BCC structure decrease, especially after yield strain. This study is significant for understanding the effect of inclusions on the mechanical laws and fracture mechanisms of the alloy.

[Fiber direction estimation using constrained spherical deconvolution based on multi-model response function].

Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus estimate fiber direction more accurately. The reconstructed fiber direction distribution is stable, the false peaks are less, and the recognition ability of cross fiber is stronger, which lays a foundation for the further research of fiber bundle tracking technology.

Prediction of the activity of Crohn's disease based on CT radiomics combined with machine learning models.

PURPOSE: To investigate the value of a CT-based radiomics model in identification of Crohn's disease (CD) active phase and remission phase. METHODS: CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS: Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION: This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance.

Diagnostic significance of heart-type fatty acid-binding protein as a potential biomarker to predict the mortality rate of patients with sepsis: a systematic review and meta-analysis.

BACKGROUND: Some studies have found that heart-type fatty acid-binding protein (H-FABP) is related to the prognosis of patients with sepsis. This study aimed to explore whether H-FABP could predict the 28-day mortality in patients with sepsis. METHODS: Seven databases were searched, and the studies were screened based on the inclusion and exclusion criteria to assess the quality. The pooled sensitivity (SEN), specificity (SPE) positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC) of the summary receiver operating characteristic (SROC) curve were calculated along with the 95% confidence interval (CI) values. Deeks' funnel plot was used to ascertain any publication bias. Meta-regression analysis was performed to explore the possible sources of heterogeneity. RESULTS: Seven studies were assessed that included 822 patients with sepsis. The pooled SEN was 0.76 (95% CI, 0.71-0.81), SPE was 0.66 (95% CI, 0.61-0.70), PLR was 2.21 (95% CI, 1.73-2.83), NLR was 0.36 (95% CI, 0.29-0.54), DOR was 6.23 (95% CI, 4.27-9.11) and the pooled AUC was 0.8137. There was no publication bias. Race, literature language, sampling time, threshold division and threshold effect were not the causes for the large heterogeneity. CONCLUSIONS: This meta-analysis suggests that H-FABP has high accuracy in predicting the 28-day mortality rate of patients with sepsis.

A retrospective study to evaluate the CT and MR imaging findings of non-Hodgkin's lymphoma affecting the jaw bones.

OBJECTIVE: To explore the CT and MR imaging findings of non-Hodgkin's lymphoma (NHL) involving the jaw bones. METHODS: Gnathic NHLs were retrospectively analyzed in 52 patients. The imaging findings were observed from the lesion number, location, size, shape, margin, density and signal intensity, CT value, bone structure change, and soft tissue mass. RESULTS: Of the 52 subjects, there were 43 with solitary gnathic NHL, and 9 with multicentric gnathic NHLs. The maximum diameter of all the 72 lesions ranged from 1.5 to 9.5 cm (mean 4.6 ± 1.67 cm). Of the 72 lesions, 90.3% were identified as irregular shapes, 87.5% as ill-defined margins, and 76.4% as homogeneous densities and signal intensities. The osteolytic destruction was found in 84.7% lesions, cortical disruption in 84.7%, and no periosteal reaction in 93.1%. The soft tissue masses surrounding the gnathic lesions were shown in 93.1% lesions. On MR imaging, the gnathic NHLs (18 lesions) showed as iso-signal intensity on T1WI, slightly higher signal intensity on T2WI, and high signal intensity on DWI. After contrast media administration, 83.3% lesions showed slightly homogeneous enhancement on T1WI. CONCLUSIONS: On CT and MR imaging, gnathic NHLs usually reveal as osteolytic lesions with irregular shapes, ill-defined margins, homogeneous densities and signal intensities, and surrounded by soft tissue mass. Some gnathic NHLs are characterized by multicentricity, which is important for indicating the condition.

Spectrum-sine interpolation framework for DTI processing.

Diffusion tensor imaging (DTI) data interpolation is important for DTI processing, which could affect the precision and computational complexity in the process of denoising, filtering, regularization, and DTI registration and fiber tracking. In this paper, we propose a novel DTI interpolation framework named with spectrum-sine (SS) focusing on tensor orientation variation in DTI processing. Compared with the state-of-the-art DTI interpolation method using Euler angles or quaternion to represent the orientation of DTI tensors, this method does not need to convert eigenvectors into Euler angles or quaternions, but interpolates each tensor's unit eigenvector directly. The prominent merit of this tensor interpolation method lies in tensor orientation information preservation, which is different from the existing DTI tensor interpolation methods that interpolating tensor's orientation information in a scalar way. The experimental results from both synthetic and real human brain DTI data demonstrated the proposed SS interpolation scheme not only maintains the advantages of Log-Euclidean and Riemannian interpolation frameworks, such as preserving the tensor's symmetric positive definiteness and the monotonic determinant variation, but also preserve the tensor's anisotropy property which was proposed in the spectral quaternion (SQ) method.

Inferior alveolar canal segmentation based on cone-beam computed tomography.

PURPOSE: The shape and position of the inferior alveolar canal (IAC) are analyzed to effectively reduce the risk of iatrogenic injury based on cone-beam computer tomography (CBCT). To assist dental clinicians to make better use of the IAC information, we propose an IAC segmentation method based on CBCT images. METHODS: In this paper, CBCT images are first preprocessed by the Hounsfield unit values clipping and gray normalization. Secondly, based on the multi-plane reconstruction (MPR) and curved surface reconstruction, the curved MPR image sets are generated by the smooth dental arch curve with a sampling distance of 1.00 pixels. Then, the K-means clustering algorithm is used to cluster the texture parameters of the gray level-gradient co-occurrence matrix enhanced by the gradient directions to improve the image contrast of the IAC. Finally, the IAC edges are roughly segmented by the 2D line-tracking method, and smoothed by the fourth-order polynomial to obtain the final segmentation result. RESULTS: Twenty-one real clinical dental CBCT datasets were used to test the proposed method. The manual segmentation results of two specialized dental clinicians were used as quantitative evaluation criteria. The dice similarity index (DSI), average symmetric surface distance (ASSD), and mean curve distance (MCD) of the left IAC are 0.93 (SD = 0.01), 0.16 mm (SD = 0.05 mm), and 1.59 mm (SD = 0.25 mm), respectively; the DSI, ASSD, and MCD of the right IAC are 0.93 (SD = 0.02), 0.16 mm (SD = 0.05 mm), and 1.60 mm (SD = 0.30 mm), respectively. CONCLUSIONS: The proposed method provides an effective image enhancement and segmentation solution to analyze the shape and position of the IAC. Experimental results show that the relationships between the IAC and other structures can be accurately reflected in the panoramic images without superimposition and geometric distortion, and the smooth edges of the IAC can be segmented.