Deep learning-based detection of irreversible pulpitis in primary molars.

BACKGROUND: Changes in healthy and inflamed pulp on periapical radiographs are traditionally so subtle that they may be imperceptible to human experts, limiting its potential use as an adjunct clinical diagnostic feature. AIM: This study aimed to investigate the feasibility of an image-analysis technique based on the convolutional neural network (CNN) to detect irreversible pulpitis in primary molars on periapical radiographs (PRs). DESIGN: This retrospective study was performed in two health centres. Patients who received indirect pulp therapy at Peking University Hospital for Stomatology were retrospectively identified and randomly divided into training and validation sets (8:2). Using PRs as input to an EfficientNet CNN, the model was trained to categorise cases into either the success or failure group and externally tested on patients who presented to our affiliate institution. Model performance was evaluated using sensitivity, specificity, accuracy and F1 score. RESULTS: A total of 348 PRs with deep caries were enrolled from the two centres. The deep learning model achieved the highest accuracy of 0.90 (95% confidence interval: 0.79-0.96) in the internal validation set, with an overall accuracy of 0.85 in the external test set. The mean greyscale value was higher in the failure group than in the success group (p = .013). CONCLUSION: The deep learning-based model could detect irreversible pulpitis in primary molars with deep caries on PRs. Moreover, this study provides a convenient and complementary method for assessing pulp status.

Identification of mIDH1 R132C/S280F Inhibitors from Natural Products by Integrated Molecular Docking, Pharmacophore Modeling and Molecular Dynamics Simulations.

Mutant isocitrate dehydrogenase 1 (mIDH1) is a common driving factor in acute myeloid leukemia (AML), with the R132 mutation accounting for a high proportion. The U.S. Food and Drug Administration (FDA) approved Ivosidenib, a molecular entity that targets IDH1 with R132 mutations, as a promising therapeutic option for AML with mIDH1 in 2018. It was of concern that the occurrence of disease resistance or recurrence, attributed to the IDH1 R132C/S280F second site mutation, was observed in certain patients treated with Ivosidenib within the same year. Furthermore, it should be noted that most mIDH1 inhibitors demonstrated limited efficacy against mutations at this specific site. Therefore, there is an urgent need to investigate novel inhibitors targeting mIDH1 for combating resistance caused by IDH1 R132C/S280F mutations in AML. This study aimed to identify novel mIDH1 R132C/S280F inhibitors through an integrated strategy of combining virtual screening and dynamics simulations. First, 2000 hits were obtained through structure-based virtual screening of the COCONUT database, and hits with better scores than -10.67 kcal/mol were obtained through molecular docking. A total of 12 potential small molecule inhibitors were identified through pharmacophore modeling screening and Prime MM-GBSA. Dynamics simulations were used to study the binding modes between the positive drug and the first three hits and IDH1 carrying the R132C/S280F mutation. RMSD showed that the four dynamics simulation systems remained stable, and RMSF and Rg showed that the screened molecules have similar local flexibility and tightness to the positive drug. Finally, the lowest energy conformation, hydrogen bond analysis, and free energy decomposition results indicate that in the entire system the key residues LEU120, TRP124, TRP267, and VAL281 mainly contribute van der Waals forces to the interaction, while the key residues VAL276 and CYS379 mainly contribute electrostatic forces.

Towards clinically applicable automated mandibular canal segmentation on CBCT.

OBJECTIVES: To develop a deep learning-based system for precise, robust, and fully automated segmentation of the mandibular canal on cone beam computed tomography (CBCT) images. METHODS: The system was developed on 536 CBCT scans (training set: 376, validation set: 80, testing set: 80) from one center and validated on an external dataset of 89 CBCT scans from 3 centers. Each scan was annotated using a multi-stage annotation method and refined by oral and maxillofacial radiologists. We proposed a three-step strategy for the mandibular canal segmentation: extraction of the region of interest based on 2D U-Net, global segmentation of the mandibular canal, and segmentation refinement based on 3D U-Net. RESULTS: The system consistently achieved accurate mandibular canal segmentation in the internal set (Dice similarity coefficient [DSC], 0.952; intersection over union [IoU], 0.912; average symmetric surface distance [ASSD], 0.046 mm; 95% Hausdorff distance [HD95], 0.325 mm) and the external set (DSC, 0.960; IoU, 0.924; ASSD, 0.040 mm; HD95, 0.288 mm). CONCLUSIONS: These results demonstrated the potential clinical application of this AI system in facilitating clinical workflows related to mandibular canal localization. CLINICAL SIGNIFICANCE: Accurate delineation of the mandibular canal on CBCT images is critical for implant placement, mandibular third molar extraction, and orthognathic surgery. This AI system enables accurate segmentation across different models, which could contribute to more efficient and precise dental automation systems.

Robust automated teeth identification from dental radiographs using deep learning.

OBJECTIVES: This study developed and validated a deep learning-based method to automatically segment and number teeth in panoramic radiographs across primary, mixed, and permanent dentitions. METHODS: A total of 6,046 panoramic radiographs were collected and annotated. The dataset encompassed primary, mixed and permanent dentitions and dental abnormalities such as tooth number anomalies, dental diseases, dental prostheses, and orthodontic appliances. A deep learning-based algorithm consisting of a U-Net-based region of interest extraction model, a Hybrid Task Cascade-based teeth segmentation and numbering model, and a post-processing procedure was trained on 4,232 images, validated on 605 images, and tested on 1,209 images. Precision, recall and Intersection-over-Union (IoU) were used to evaluate its performance. RESULTS: The deep learning-based teeth identification algorithm achieved good performance on panoramic radiographs, with precision and recall for teeth segmentation and numbering exceeding 97%, and the IoU between predictions and ground truths reaching 92%. It generalized well across all three dentition stages and complex real-world cases. CONCLUSIONS: By utilizing a two-stage training framework with a large-scale heterogeneous dataset, the automatic teeth identification algorithm achieved a performance level comparable to that of dental experts. CLINICAL SIGNIFICANCE: Deep learning can be leveraged to aid clinical interpretation of panoramic radiographs across primary, mixed, and permanent dentitions, even in the presence of real-world complexities. This robust teeth identification algorithm could contribute to the future development of more advanced, diagnosis- or treatment-oriented dental automation systems.

Convolutional neural network models for automatic diagnosis and graduation in skin frostbite.

The study aimed to develop and validate a convolutional neural network (CNN)-based deep learning method for automatic diagnosis and graduation of skin frostbite. A dataset of 71 annotated images was used for the training, the validation, and the testing based on ResNet-50 model. The performances were evaluated with the test set. The diagnosis and graduation performance of our approach was compared with two residents from burns department. The approach correctly identified all the frostbite of IV (18/18, 100%), but with respectively 1 mistake in the diagnosis of degree I (29/30, 96.67%), II (28/29, 96.55%) and III (37/38, 97.37%). The accuracy of the approach on the whole test set was 97.39% (112/115). The accuracy of the two residents were respectively 77.39% and 73.04%. Weighted Kappa of 0.583 indicates good reliability between the two residents (P = .445). Kendall's coefficient of concordance is 0.326 (P = .548), indicating differences in accuracy between the approach and the two residents. Our approach based on CNNs demonstrated an encouraging performance for the automatic diagnosis and graduation of skin frostbite, with higher accuracy and efficiency.

Aging Interfacial Structure and Abnormal Tensile Strength of SnAg3Cu0.5/Cu Solder Joints.

In this study, the interfacial structure and abnormal long-term increase of tensile strength in the interfacial intermetallic compounds (IMCs) between SnAg3Cu0.5 solder and Cu substrates during isothermal aging were investigated. After reflow soldering, the IMC layer at the interface was thin and scallop-type. The interfacial layer became thicker with the increase in aging time. After 200 h of aging at 150 °C, the thickness of the interface gradually increased to 3.93 µm and the interface became smooth. Compared with the unaged Cu-Sn interface, the aged joint interface contained more Cu3Sn. The top of the IMC being reflown was relatively smooth, but became denser and prismatic in shape after 200 h of aging at 150 °C. The tensile strength of the joint, immediately after the reflow, reached 81.93 MPa. The tensile properties of the solder joints weakened and then strengthened as they aged. After 200 h of aging at 150 °C, the tensile strength was 83.86 MPa, which exceeded that of the unaged solder joint interface, because the fracture mode of the solder joints changed during aging.

Detection and classification of mandibular fracture on CT scan using deep convolutional neural network.

OBJECTIVES: This study aimed to evaluate the accuracy and reliability of convolutional neural networks (CNNs) for the detection and classification of mandibular fracture on spiral computed tomography (CT). MATERIALS AND METHODS: Between January 2013 and July 2020, 686 patients with mandibular fractures who underwent CT scan were classified and annotated by three experienced maxillofacial surgeons serving as the ground truth. An algorithm including two convolutional neural networks (U-Net and ResNet) was trained, validated, and tested using 222, 56, and 408 CT scans, respectively. The diagnostic performance of the algorithm was compared with the ground truth and evaluated by DICE, accuracy, sensitivity, specificity, and area under the ROC curve (AUC). RESULTS: One thousand five hundred six mandibular fractures in nine subregions of 686 patients were diagnosed. The DICE of mandible segmentation using U-Net was 0.943. The accuracies of nine subregions were all above 90%, with a mean AUC of 0.956. CONCLUSIONS: CNNs showed comparable reliability and accuracy in detecting and classifying mandibular fractures on CT. CLINICAL RELEVANCE: The algorithm for automatic detection and classification of mandibular fractures will help improve diagnostic efficiency and provide expertise to areas with lower medical levels.

Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT.

OBJECTIVES: The objective of our study was to develop and validate a deep learning approach based on convolutional neural networks (CNNs) for automatic detection of the mandibular third molar (M3) and the mandibular canal (MC) and evaluation of the relationship between them on CBCT. MATERIALS AND METHODS: A dataset of 254 CBCT scans with annotations by radiologists was used for the training, the validation, and the test. The proposed approach consisted of two modules: (1) detection and pixel-wise segmentation of M3 and MC based on U-Nets; (2) M3-MC relation classification based on ResNet-34. The performances were evaluated with the test set. The classification performance of our approach was compared with two residents in oral and maxillofacial radiology. RESULTS: For segmentation performance, the M3 had a mean Dice similarity coefficient (mDSC) of 0.9730 and a mean intersection over union (mIoU) of 0.9606; the MC had a mDSC of 0.9248 and a mIoU of 0.9003. The classification models achieved a mean sensitivity of 90.2%, a mean specificity of 95.0%, and a mean accuracy of 93.3%, which was on par with the residents. CONCLUSIONS: Our approach based on CNNs demonstrated an encouraging performance for the automatic detection and evaluation of the M3 and MC on CBCT. Clinical relevance An automated approach based on CNNs for detection and evaluation of M3 and MC on CBCT has been established, which can be utilized to improve diagnostic efficiency and facilitate the precision diagnosis and treatment of M3.

Theoretical prediction of the impact sensitivities of energetic C-nitro compounds.

In order to design high-energetic and insensitive explosives, the frontier orbital energy gaps, surface electrostatic potentials, nitro group charges, bond dissociation energies (BDEs) of the C-NO2 trigger bonds, and intermolecular interactions obtained by the M06-2X/6-311++G(2d,p) method were quantitatively correlated with the experimental drop hammer potential energies of 10 typical C-nitro explosives. The changes of several information-theoretic quantities (ITQs) in the density functional reactivity theory were discussed upon the formation of complexes. The BDEs in the explosives with six-membered ring are larger than those with five-membered ring. The frontier orbital energy gaps of the compounds with benzene ring are larger than those with N-heterocycle. The models involving the intermolecular interaction energies and the energy gaps could be used to predict the impact sensitivity of the C-nitro explosives, while those involving ΔSS, ΔIF, and ΔSGBP are invalid. With the more and more ITQs, the further studies are needed to seek for a good correlation between impact sensitivity measurements and ITQs for the energetic C-nitro compounds. The origin of sensitivity was revealed by the reduced density gradient method.

Five-year long-term outcomes of laparoscopic surgery for colon cancer.

AIM: To perform a meta-analysis to answer whether long-term recurrence rates after laparoscopic-assisted surgery are comparable to those reported after open surgery. METHODS: A comprehensive literature search of the MEDLINE database, EMBASE database, and the Cochrane Central Register of Controlled Trials for the years 1991-2010 was performed. Prospective randomized clinical trials (RCTs) were eligible if they included patients with colon cancer treated by laparoscopic surgery vs open surgery and followed for more than five years. RESULTS: Three studies involving 2147 patients reported long-term outcomes based on five-year data and were included in the analysis. The overall mortality was similar in the two groups (24.9%, 268/1075 in the laparoscopic group and 26.4%, 283/1072 in open group). No significant differences between laparoscopic and open surgery were found in overall mortality during the follow-up period of these studies [OR (fixed) 0.92, 95% confidence intervals (95% CI): 0.76-1.12, P = 0.41]. No significant difference in the development of overall recurrence was found in colon cancer patients, when comparing laparoscopic and open surgery [2147 pts, 19.3% vs 20.0%; OR (fixed) 0.96, 95% CI: 0.78-1.19, P = 0.71]. CONCLUSION: This meta-analysis suggests that laparoscopic surgery was as efficacious and safe as open surgery for colon cancer, based on the five-year data of these included RCTs.

Aspirin use and the risk of gastric cancer: a meta-analysis.

PURPOSE: Studies investigating the association between aspirin use and gastric cancer risk have reported conflicting results. The objective of this study was to quantitatively summarize the evidence for such a relationship. RESULTS: Two investigators independently searched the Medline, PubMed, Embase, and Academic Search Premier (EBSCO) databases. Fourteen studies with a total number of 5,640 gastric cancer cases were identified. Most of the study populations were Caucasian. The combined results based on all studies showed there was no statistically significant difference between aspirin use and gastric cancer risk (odds ratio (OR) = 0.80, 95% confidence intervals (CI) = 0.54-1.19). When stratifying by study designs and gender, results were similar except for cohort and randomized controlled trial (RCT) studies (OR = 0.72, 95% CI = 0.62-0.84). When stratifying by location and Helicobacter pylori (H. pylori) infection, we observed there were lower risks in noncardia gastric cancer (OR = 0.62, 95% CI = 0.55-0.69) and H. pylori-infected individuals (OR = 0.62, 95% CI = 0.42-0.90) for aspirin users. Among Caucasians, there were lower risks for noncardia gastric cancer (OR = 0.73, 95% CI = 0.62-0.87) and H. pylori-infected individuals (OR = 0.62, 95% CI = 0.42-0.90) also. CONCLUSIONS: This meta-analysis indicated that regular use of aspirin may be associated with reduced risk of noncardia gastric cancer, especially among Caucasians; for H. pylori-infected subjects the result was similar.

Overweight, obesity and gastric cancer risk: results from a meta-analysis of cohort studies.

The relationship between excess body weight and gastric cancer risk has not been well studied to date. We therefore carried out a systematic review and meta-analysis of published cohort studies to evaluate the association between excess body weight and gastric cancer risk. An electronic search of the MEDLINE, PubMed, EMBASE and Academic Search Premier (EBSCO) databases, which contain articles published from 1950 onwards, was conducted in order to select studies for this meta-analysis. Ten studies with a total number of 9492 gastric cancer cases and a studied population of 3,097,794 were identified. Overall, excess body weight [body mass index (BMI)25] was associated with an increased risk of gastric cancer [odds ratio (OR)=1.22; 95% confidence intervals (CIs)=1.06-1.41]. Specifically, a stratified analysis showed that excess body weight was associated with an increased risk of cardia gastric cancer [overweight and obese (BMI 25), OR=1.55, 95% CIs=1.31-1.84] and gastric cancer among non-Asians (overweight and obese, OR=1.24, 95% CIs=1.14-1.36); however, the stratified analysis also showed that there was no statistically significant link between excess body weight and gastric cancer in the following subgroups: males (overweight and obese, OR=1.22, 95% CIs=0.96-1.55), females (overweight and obese, OR=1.13, 95% CIs=0.65-1.94), non-cardia gastric cancer (overweight and obese, OR=1.18, 95% CIs=0.96-1.45) and Asians (overweight and obese, OR=1.17, 95% CIs=0.88-1.56). The combined results of this meta-analysis, however, do indicate that overweight and obesity are associated with an increased risk of gastric cancer. The strength of the association also increases with increasing BMI.