Ubiquitin regulatory X (UBX) domain-containing protein 6 is essential for autophagy induction and inflammation control in macrophages.

Ubiquitin regulatory X (UBX) domain-containing protein 6 (UBXN6) is an essential cofactor for the activity of the valosin-containing protein p97, an adenosine triphosphatase associated with diverse cellular activities. Nonetheless, its role in cells of the innate immune system remains largely unexplored. In this study, we report that UBXN6 is upregulated in humans with sepsis and may serve as a pivotal regulator of inflammatory responses via the activation of autophagy. Notably, the upregulation of UBXN6 in sepsis patients was negatively correlated with inflammatory gene profiles but positively correlated with the expression of Forkhead box O3, an autophagy-driving transcription factor. Compared with those of control mice, the macrophages of mice subjected to myeloid cell-specific UBXN6 depletion exhibited exacerbated inflammation, increased mitochondrial oxidative stress, and greater impairment of autophagy and endoplasmic reticulum-associated degradation pathways. UBXN6-deficient macrophages also exhibited immunometabolic remodeling, characterized by a shift to aerobic glycolysis and elevated levels of branched-chain amino acids. These metabolic shifts amplify mammalian target of rapamycin pathway signaling, in turn reducing the nuclear translocation of the transcription factor EB and impairing lysosomal biogenesis. Together, these data reveal that UBXN6 serves as an activator of autophagy and regulates inflammation to maintain immune system suppression during human sepsis.

Arenicolide Family Macrolides Provide a New Therapeutic Lead Combating Multidrug-resistant Tuberculosis.

The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis (Mtb) poses a significant threat to health globally. During searching for new chemical entities regulating MDR- and XDR-Mtb, chemical investigation of the black oil beetle gut bacterium Micromonospora sp. GR10 led to the discovery of eight new members of arenicolides along with the identification of arenicolide A (Ar-A, 1), which was a previously reported macrolide with incomplete configuration. Genomic analysis of the bacterial strain GR10 revealed their putative biosynthetic pathway. Quantum mechanics-based computation, chemical derivatizations, and bioinformatic analysis established the absolute stereochemistry of Ar-A and arenicolides D-K (Ar-D-K, 2-9) completely for the first time. Biological studies of 1-9 revealed their antimicrobial activity against MDR and XDR strains of Mtb. Ar-A had the most potent in vitro antimicrobial efficacy against MDR- and XDR-Mtb. Mechanistically, Ar-A induced ATP depletion and destabilized Mtb cell wall, thereby inhibiting growth. Notably, Ar-A exerted a significant antimicrobial effect against Mtb in macrophages, was effective in the treatment of Mtb infections, and showed a synergistic effect with amikacin (AMK) in a mouse model of MDR-Mtb lung infection. Collectively, our findings indicate Ar-A to be a promising drug lead for drug-resistant tuberculosis.

Effects of the Strength, Assistance in walking, Rise from a chair, Climb stairs, and Falls score on postoperative clinical outcomes following colorectal cancer surgery: a retrospective study.

BACKGROUND/OBJECTIVES: Sarcopenia has known negative effects on clinical and oncological outcomes in patients with colorectal cancer (CRC). The use of the Strength, Assistance in walking, Rise from a chair, Climb stairs, and Falls (SARC-F) questionnaire to determine the effects of sarcopenia on postoperative complications of CRC has not been reported to date. Therefore, this study aimed to investigate the relationship of SARC-F score with clinicopathologic outcomes after CRC surgery. SUBJECTS/METHODS: We retrospectively included 285 patients who completed SARC-F questionnaires before CRC surgery between July 2019 and March 2022. Patients with an SARC-F score ≥4 (total score: 10) were classified in the high SARC-F group. RESULTS: Overall, 34 (11.9%) patients had high SARC-F scores. These patients were older (76.9 ± 8.5 vs. 64.5 ± 11.4 years, p < 0.001) and had a higher preoperative CRP (2.5 ± 3.9 vs. 0.8 ± 1.6 mg/L, p = 0.019), lower body mass index (21.7 ± 3.4 vs. 24.0 ± 3.8 kg/m2, p = 0.001), and higher pan-immune-inflammation value (632.3 ± 615.5 vs. 388.9 ± 413.8, p = 0.031). More postoperative complications were noted in the high SARC-F group than in the low SARC-F group (58.8% vs. 35.6%, p = 0.009). High SARC-F scores were significantly associated with higher nodal stage, higher number of harvested lymph nodes, and larger tumor size. Univariate and multivariate analyses revealed high SARC-F score and operation time as independent risk factors associated with postoperative complications (odds ratio, 2.212/1.922; 95% confidence interval, 1.021-4.792/1.163-3.175; p = 0.044/0.011, respectively). CONCLUSIONS: Preoperative SARC-F score was an independent risk factor associated with postoperative complications following colorectal cancer surgery.

Automated Association for Osteosynthesis Foundation and Orthopedic Trauma Association classification of pelvic fractures on pelvic radiographs using deep learning.

High-energy impacts, like vehicle crashes or falls, can lead to pelvic ring injuries. Rapid diagnosis and treatment are crucial due to the risks of severe bleeding and organ damage. Pelvic radiography promptly assesses fracture extent and location, but struggles to diagnose bleeding. The AO/OTA classification system grades pelvic instability, but its complexity limits its use in emergency settings. This study develops and evaluates a deep learning algorithm to classify pelvic fractures on radiographs per the AO/OTA system. Pelvic radiographs of 773 patients with pelvic fractures and 167 patients without pelvic fractures were retrospectively analyzed at a single center. Pelvic fractures were classified into types A, B, and C using medical records categorized by an orthopedic surgeon according to the AO/OTA classification system. Accuracy, Dice Similarity Coefficient (DSC), and F1 score were measured to evaluate the diagnostic performance of the deep learning algorithms. The segmentation model showed high performance with 0.98 accuracy and 0.96-0.97 DSC. The AO/OTA classification model demonstrated effective performance with a 0.47-0.80 F1 score and 0.69-0.88 accuracy. Additionally, the classification model had a macro average of 0.77-0.94. Performance evaluation of the models showed relatively favorable results, which can aid in early classification of pelvic fractures.

Secular changes in dental development of Korean children aged 4 to 16 years over a 10-year period.

This study evaluated 10-year secular changes in dental maturity and dental development among Korean children. A retrospective analysis of panoramic radiograph samples from Korean children (4-16 years old) taken in 2010 and 2020 was conducted. The 2010 group consisted of 3491 radiographs (1970 boys and 1521 girls), and the 2020 group included 5133 radiographs (2825 boys and 2308 girls). Using Demirjian's method, dental maturity scores and dental developmental stages were assessed. For intra-observer reliability, Weighted Cohen's kappa was used, and Mann-Whitney U tests were performed to compare the 2020 and 2010 groups. A slight acceleration in dental maturity was observed in both boys and girls, with the difference being more noticeable in boys at an earlier age. Statistically significant differences were noted at ages 4, 5 and 7 for boys, and at age 6 for girls. Despite these differences, the individual dental development stages of 2020 and 2010 showed inconsistent trends with limited differences. Generally, girls demonstrate more advanced dental maturity than boys. A slight acceleration in Korean children's dental maturity was observed over a 10-year period when comparing the 2020 groups to the 2010 groups.

Interfacial Engineering of Mn Porphyrin/Au Electrodes for Identifying MnOx as the Active Species under Oxygen Evolution Reactions.

Understanding the influence of surface structural features at a molecular level is beneficial in guiding an electrode's mechanistic proposals for electrocatalytic reactions. The relationship between structural stability and catalytic activity significantly impacts reaction performance, even though atomistic knowledge of active sites remains a topic of discussion. In this context, this work presents scanning tunneling microscopy (STM) results for the highly ordered arrangement of manganese porphyrin molecules on a Au(111) surface; STM allows us to monitor active sites at a molecular level to focus on long-standing issues with the electrocatalytic process, especially the exact nature of the real active sites at the interfaces. After water oxidation, manganese porphyrin rapidly decomposes into active catalytic species as bright protrusions. These newly formed active species drastically lost catalytic activity, up to 82%, through only acid treatment, one of the oxide removal methods, not by deionized water and acetone treatments. STM results of the obviated active species on the Au surface by an acidic solution support the forfeited catalytic activity. In addition, it shows a 67% decrement in catalytic activity by adsorption of phosphonic acid, one of the oxide's preferred adsorption materials, compared to the pristine one. Based on these observations, we confirm that the newly formed active species, as water oxidation catalysts, mostly consist of manganese oxides. Notable findings of our work provide molecular evidence for the active sites of Au and modified Au electrodes that spur the future development of water oxidation catalysts.

Intraoperative detection of parathyroid glands using artificial intelligence: optimizing medical image training with data augmentation methods.

BACKGROUND: Postoperative hypoparathyroidism is a major complication of thyroidectomy, occurring when the parathyroid glands are inadvertently damaged during surgery. Although intraoperative images are rarely used to train artificial intelligence (AI) because of its complex nature, AI may be trained to intraoperatively detect parathyroid glands using various augmentation methods. The purpose of this study was to train an effective AI model to detect parathyroid glands during thyroidectomy. METHODS: Video clips of the parathyroid gland were collected during thyroid lobectomy procedures. Confirmed parathyroid images were used to train three types of datasets according to augmentation status: baseline, geometric transformation, and generative adversarial network-based image inpainting. The primary outcome was the average precision of the performance of AI in detecting parathyroid glands. RESULTS: 152 Fine-needle aspiration-confirmed parathyroid gland images were acquired from 150 patients who underwent unilateral lobectomy. The average precision of the AI model in detecting parathyroid glands based on baseline data was 77%. This performance was enhanced by applying both geometric transformation and image inpainting augmentation methods, with the geometric transformation data augmentation dataset showing a higher average precision (79%) than the image inpainting model (78.6%). When this model was subjected to external validation using a completely different thyroidectomy approach, the image inpainting method was more effective (46%) than both the geometric transformation (37%) and baseline (33%) methods. CONCLUSION: This AI model was found to be an effective and generalizable tool in the intraoperative identification of parathyroid glands during thyroidectomy, especially when aided by appropriate augmentation methods. Additional studies comparing model performance and surgeon identification, however, are needed to assess the true clinical relevance of this AI model.

Allergy Wheal and Erythema Segmentation Using Attention U-Net.

The skin prick test (SPT) is a key tool for identifying sensitized allergens associated with immunoglobulin E-mediated allergic diseases such as asthma, allergic rhinitis, atopic dermatitis, urticaria, angioedema, and anaphylaxis. However, the SPT is labor-intensive and time-consuming due to the necessity of measuring the sizes of the erythema and wheals induced by allergens on the skin. In this study, we used an image preprocessing method and a deep learning model to segment wheals and erythema in SPT images captured by a smartphone camera. Subsequently, we assessed the deep learning model's performance by comparing the results with ground-truth data. Using contrast-limited adaptive histogram equalization (CLAHE), an image preprocessing technique designed to enhance image contrast, we augmented the chromatic contrast in 46 SPT images from 33 participants. We established a deep learning model for wheal and erythema segmentation using 144 and 150 training datasets, respectively. The wheal segmentation model achieved an accuracy of 0.9985, a sensitivity of 0.5621, a specificity of 0.9995, and a Dice similarity coefficient of 0.7079, whereas the erythema segmentation model achieved an accuracy of 0.9660, a sensitivity of 0.5787, a specificity of 0.97977, and a Dice similarity coefficient of 0.6636. The use of image preprocessing and deep learning technology in SPT is expected to have a significant positive impact on medical practice by ensuring the accurate segmentation of wheals and erythema, producing consistent evaluation results, and simplifying diagnostic processes.

Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography.

BACKGROUND: We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography. METHODS: Panoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 3:1:1. RESULTS: To evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43-94.26%, 52.63-60.77%, 72.01-75.84%, and 76.36-79.00%, respectively. CONCLUSION: We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.

Airway and Airway Obstruction Site Segmentation Study Using U-Net with Drug-Induced Sleep Endoscopy Images.

Obstructive sleep apnea is characterized by a decrease or cessation of breathing due to repetitive closure of the upper airway during sleep, leading to a decrease in blood oxygen saturation. In this study, employing a U-Net model, we utilized drug-induced sleep endoscopy images to segment the major causes of airway obstruction, including the epiglottis, oropharynx lateral walls, and tongue base. The evaluation metrics included sensitivity, specificity, accuracy, and Dice score, with airway sensitivity at 0.93 (± 0.06), specificity at 0.96 (± 0.01), accuracy at 0.95 (± 0.01), and Dice score at 0.84 (± 0.03), indicating overall high performance. The results indicate the potential for artificial intelligence (AI)-driven automatic interpretation of sleep disorder diagnosis, with implications for standardizing medical procedures and improving healthcare services. The study suggests that advancements in AI technology hold promise for enhancing diagnostic accuracy and treatment efficacy in sleep and respiratory disorders, fostering competitiveness in the medical AI market.

Re-hospitalization factors and economic characteristics of urinary tract infected patients using machine learning.

Objective: Urinary tract infection is one of the most prevalent bacterial infectious diseases in outpatient treatment, and 50-80% of women experience it more than once, with a recurrence rate of 40-50% within a year; consequently, preventing re-hospitalization of patients is critical. However, in the field of urology, no research on the analysis of the re-hospitalization status for urinary tract infections using machine learning algorithms has been reported to date. Therefore, this study uses various machine learning algorithms to analyze the clinical and nonclinical factors related to patients who were re-hospitalized within 30 days of urinary tract infection. Methods: Data were collected from 497 patients re-hospitalized for urinary tract infections within 30 days and 496 patients who did not require re-hospitalization. The re-hospitalization factors were analyzed using four machine learning algorithms: gradient boosting classifier, random forest, naive Bayes, and logistic regression. Results: The best-performing gradient boosting classifier identified respiratory rate, days of hospitalization, albumin, diastolic blood pressure, blood urea nitrogen, body mass index, systolic blood pressure, body temperature, total bilirubin, and pulse as the top-10 factors that affect re-hospitalization because of urinary tract infections. The 993 patients whose data were collected were divided into risk groups based on these factors, and the re-hospitalization rate, days of hospitalization, and medical expenses were observed to decrease from the high- to low-risk group. Conclusions: This study showed new possibilities in analyzing the status of urinary tract infection-related re-hospitalization using machine learning. Identifying factors affecting re-hospitalization and incorporating preventable and reinforcement-based treatment programs can aid in reducing the re-hospitalization rate and average number of days of hospitalization, thereby reducing medical expenses.

The autophagy-targeting compound V46 enhances antimicrobial responses to Mycobacteroides abscessus by activating transcription factor EB.

Mycobacteroides abscessus (Mabc) is a rapidly growing nontuberculous mycobacterium that poses a considerable challenge as a multidrug-resistant pathogen causing chronic human infection. Effective therapeutics that enhance protective immune responses to Mabc are urgently needed. This study introduces trans-3,5,4'-trimethoxystilbene (V46), a novel resveratrol analogue with autophagy-activating properties and antimicrobial activity against Mabc infection, including multidrug-resistant strains. Among the resveratrol analogues tested, V46 significantly inhibited the growth of both rough and smooth Mabc strains, including multidrug-resistant strains, in macrophages and in the lungs of mice infected with Mabc. Additionally, V46 substantially reduced Mabc-induced levels of pro-inflammatory cytokines and chemokines in both macrophages and during in vivo infection. Mechanistic analysis showed that V46 suppressed the activation of the protein kinase B/Akt-mammalian target of rapamycin signaling pathway and enhanced adenosine monophosphate-activated protein kinase signaling in Mabc-infected cells. Notably, V46 activated autophagy and the nuclear translocation of transcription factor EB, which is crucial for antimicrobial host defenses against Mabc. Furthermore, V46 upregulated genes associated with autophagy and lysosomal biogenesis in Mabc-infected bone marrow-derived macrophages. The combination of V46 and rifabutin exerted a synergistic antimicrobial effect. These findings identify V46 as a candidate host-directed therapeutic for Mabc infection that activates autophagy and lysosomal function via transcription factor EB.

Radiomics and machine learning analysis of liver magnetic resonance imaging for prediction and early detection of tumor response in colorectal liver metastases.

PURPOSE: The aim of this study was to demonstrate the effectiveness of a machine learning-based radiomics model for distinguishing tumor response and overall survival in patients with unresectable colorectal liver metastases (CRLM) treated with targeted biological therapy. METHODS: We prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy. We evaluated the diagnostic performance of machine learning-based radiomics model in tumor response of liver MRI compared with the guidelines for the Response Evaluation Criteria in Solid Tumors. Overall survival was evaluated using the Kaplan-Meier analysis and compared to the Cox proportional hazard ratios following univariate and multivariate analyses. RESULTS: Performance measurement of the trained model through metrics showed the accuracy of the machine learning model to be 76.5%, and the area under the receiver operating characteristic curve was 0.857 (95% confidence interval [CI], 0.605-0.976; P < 0.001). For the patients classified as non-progressing or progressing by the radiomics model, the median overall survival was 17.5 months (95% CI, 12.8-22.2), and 14.8 months (95% CI, 14.2-15.4), respectively (P = 0.431, log-rank test). CONCLUSION: Machine learning-based radiomics models could have the potential to predict tumor response in patients with unresectable CRLM treated with biologic therapy.

Deep Learning-Based Nystagmus Detection for BPPV Diagnosis.

In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystagmus detection models. Among the four deep learning architectures used in this study, the CNN1D model proposed as a nystagmus detection model demonstrated the best performance, exhibiting a sensitivity of 94.06 ± 0.78%, specificity of 86.39 ± 1.31%, precision of 91.34 ± 0.84%, accuracy of 91.02 ± 0.66%, and an F1-score of 92.68 ± 0.55%. These results indicate the high accuracy and generalizability of the proposed nystagmus diagnosis algorithm. In conclusion, this study validates the practicality of deep learning in diagnosing BPPV and offers avenues for numerous potential applications of deep learning in the medical diagnostic sector. The findings of this research underscore its importance in enhancing diagnostic accuracy and efficiency in healthcare.

Alterations in pH of Coffee Bean Extract and Properties of Chlorogenic Acid Based on the Roasting Degree.

Factors influencing the sour taste of coffee and the properties of chlorogenic acid are not yet fully understood. This study aimed to evaluate the impact of roasting degree on pH-associated changes in coffee bean extract and the thermal stability of chlorogenic acid. Coffee bean extract pH decreased up to a chromaticity value of 75 but increased with higher chromaticity values. Ultraviolet-visible spectrophotometry and structural analysis attributed this effect to chlorogenic and caffeic acids. Moreover, liquid chromatography-mass spectrometry analysis identified four chlorogenic acid types in green coffee bean extract. Chlorogenic acid isomers were eluted broadly on HPLC, and a chlorogenic acid fraction graph with two peaks, fractions 5 and 9, was obtained. Among the various fractions, the isomer in fraction 5 had significantly lower thermal stability, indicating that thermal stability differs between chlorogenic acid isomers.

Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs.

Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock. Pelvic radiography can quickly diagnose pelvic fractures, and the Association for Osteosynthesis Foundation and Orthopedic Trauma Association (AO/OTA) classification system is useful for evaluating pelvic fracture instability. This study aimed to develop a radiomics-based machine-learning algorithm to quickly diagnose fractures on pelvic X-ray and classify their instability. data used were pelvic anteroposterior radiographs of 990 adults over 18 years of age diagnosed with pelvic fractures, and 200 normal subjects. A total of 93 features were extracted based on radiomics:18 first-order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM features. To improve the performance of machine learning, the feature selection methods RFE, SFS, LASSO, and Ridge were used, and the machine learning models used LR, SVM, RF, XGB, MLP, KNN, and LGBM. Performance measurement was evaluated by area under the curve (AUC) by analyzing the receiver operating characteristic curve. The machine learning model was trained based on the selected features using four feature-selection methods. When the RFE feature selection method was used, the average AUC was higher than that of the other methods. Among them, the combination with the machine learning model SVM showed the best performance, with an average AUC of 0.75±0.06. By obtaining a feature-importance graph for the combination of RFE and SVM, it is possible to identify features with high importance. The AO/OTA classification of normal pelvic rings and pelvic fractures on pelvic AP radiographs using a radiomics-based machine learning model showed the highest AUC when using the SVM classification combination. Further research on the radiomic features of each part of the pelvic bone constituting the pelvic ring is needed.

Image segmentation of impacted mesiodens using deep learning.

This study aimed to evaluate the performance of deep learning algorithms for the classification and segmentation of impacted mesiodens in pediatric panoramic radiographs. A total of 850 panoramic radiographs of pediatric patients (aged 3-9 years) was included in this study. The U-Net semantic segmentation algorithm was applied for the detection and segmentation of mesiodens in the upper anterior region. For enhancement of the algorithm, pre-trained ResNet models were applied to the encoding path. The segmentation performance of the algorithm was tested using the Jaccard index and Dice coefficient. The diagnostic accuracy, precision, recall, F1-score and time to diagnosis of the algorithms were compared with those of human expert groups using the test dataset. Cohen's kappa statistics were compared between the model and human groups. The segmentation model exhibited a high Jaccard index and Dice coefficient (>90%). In mesiodens diagnosis, the trained model achieved 91-92% accuracy and a 94-95% F1-score, which were comparable with human expert group results (96%). The diagnostic duration of the deep learning model was 7.5 seconds, which was significantly faster in mesiodens detection compared to human groups. The agreement between the deep learning model and human experts is moderate (Cohen's kappa = 0.767). The proposed deep learning algorithm showed good segmentation performance and approached the performance of human experts in the diagnosis of mesiodens, with a significantly faster diagnosis time.

Deep-learning-based pelvic automatic segmentation in pelvic fractures.

With the recent increase in traffic accidents, pelvic fractures are increasing, second only to skull fractures, in terms of mortality and risk of complications. Research is actively being conducted on the treatment of intra-abdominal bleeding, the primary cause of death related to pelvic fractures. Considerable preliminary research has also been performed on segmenting tumors and organs. However, studies on clinically useful algorithms for bone and pelvic segmentation, based on developed models, are limited. In this study, we explored the potential of deep-learning models presented in previous studies to accurately segment pelvic regions in X-ray images. Data were collected from X-ray images of 940 patients aged 18 or older at Gachon University Gil Hospital from January 2015 to December 2022. To segment the pelvis, Attention U-Net, Swin U-Net, and U-Net were trained, thereby comparing and analyzing the results using five-fold cross-validation. The Swin U-Net model displayed relatively high performance compared to Attention U-Net and U-Net models, achieving an average sensitivity, specificity, accuracy, and dice similarity coefficient of 96.77%, of 98.50%, 98.03%, and 96.32%, respectively.

Fully automated deep learning approach to dental development assessment in panoramic radiographs.

BACKGROUND: Dental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian's method using deep learning. METHODS: The study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021. The proposed methodology involves a three-step procedure for dental staging: detection, segmentation, and classification. The panoramic data were randomly divided into training and validating sets (8:2), and YOLOv5, U-Net, and EfficientNet were trained and employed for each stage. The models' performance, along with the Grad-CAM analysis of EfficientNet, was evaluated. RESULTS: The mean average precision (mAP) was 0.995 for detection, and the segmentation achieved an accuracy of 0.978. The classification performance showed F1 scores of 69.23, 80.67, 84.97, and 90.81 for the Incisor, Canine, Premolar, and Molar models, respectively. In the Grad-CAM analysis, the classification model focused on the apical portion of the developing tooth, a crucial feature for staging according to Demirjian's method. CONCLUSIONS: These results indicate that the proposed deep learning approach for automated dental staging can serve as a supportive tool for dentists, facilitating rapid and objective dental age estimation and dental maturity evaluation.

Deep Learning-Based Multi-Class Segmentation of the Paranasal Sinuses of Sinusitis Patients Based on Computed Tomographic Images.

Accurate paranasal sinus segmentation is essential for reducing surgical complications through surgical guidance systems. This study introduces a multiclass Convolutional Neural Network (CNN) segmentation model by comparing four 3D U-Net variations-normal, residual, dense, and residual-dense. Data normalization and training were conducted on a 40-patient test set (20 normal, 20 abnormal) using 5-fold cross-validation. The normal 3D U-Net demonstrated superior performance with an F1 score of 84.29% on the normal test set and 79.32% on the abnormal set, exhibiting higher true positive rates for the sphenoid and maxillary sinus in both sets. Despite effective segmentation in clear sinuses, limitations were observed in mucosal inflammation. Nevertheless, the algorithm's enhanced segmentation of abnormal sinuses suggests potential clinical applications, with ongoing refinements expected for broader utility.