Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

Background: Limited data are available on the mortality rates of patients receiving extracorporeal membrane oxygenation (ECMO) support for coronavirus disease 2019 (COVID-19). We aimed to analyze the relationship between COVID-19 and clinical outcomes for patients receiving ECMO. Methods: We retrospectively investigated patients with COVID-19 pneumonia requiring ECMO in 19 hospitals across Korea from January 1, 2020 to August 31, 2021. The primary outcome was the 90-day mortality after ECMO initiation. We performed multivariate analysis using a logistic regression model to estimate the odds ratio (OR) of 90-day mortality. Survival differences were analyzed using the Kaplan–Meier (KM) method. Results: Of 127 patients with COVID-19 pneumonia who received ECMO, 70 patients (55.1%) died within 90 days of ECMO initiation. The median age was 64 years, and 63% of patients were male. The incidence of ECMO was increased with age but was decreased after 70 years of age. However, the survival rate was decreased linearly with age. In multivariate analysis, age (OR, 1.048; 95% confidence interval [CI], 1.010–1.089; P = 0.014) and receipt of continuous renal replacement therapy (CRRT) (OR, 3.069; 95% CI, 1.312–7.180; P = 0.010) were significantly associated with an increased risk of 90-day mortality. KM curves showed significant differences in survival between groups according to age (65 years) (log-rank P = 0.021) and receipt of CRRT (log-rank P = 0.004). Conclusion Older age and receipt of CRRT were associated with higher mortality rates among patients with COVID-19 who received ECMO.

Objective: This study evaluated the accuracy of the pre-hospital shock index multiplied by the AVPU scale (PSIAVPU) as a predictor of massive transfusion (MT) and traumatic coagulopathy. Methods: This research was a retrospective single-center study that included patients consecutively presenting to a trauma center between 2017 and 2020. The predictive value of the PSIAVPU for MT, in-hospital mortality, and traumatic coagulopathy was measured using the area under the curve (AUC) of the receiver operating characteristic curve. The AUC of the PSIAVPU was compared with the Reverse Shock Index multiplied by the Glasgow Coma Scale (rSIG) measured at the trauma center presentation. Results: One thousand seven hundred and ninety-two patients were included, of which 163 patients (9.09%) received MT and 195 patients (10.88%) died during their hospital stay. Traumatic coagulopathy was observed in 245 patients. The AUC values for the PSIAVPU in terms of predicting MT, hospital mortality, and traumatic coagulopathy were 0.755, 0.752, and 0.736, respectively. Conclusion In patients with trauma, the predictive power of the PSIAVPU was higher than that of the prehospital shock index and was comparable to that of the rSIG. The PSIAVPU is a useful indicator that can be used easily and quickly for trauma patients at the prehospital stage.

Nodular fasciitis (NF) is a benign myofibroblastic proliferation that grows very rapidly, mimicking a sarcoma on imaging. It is treated by local excision, and recurrence has been reported in only a few cases, even when excised incompletely. The most prevalent diagnoses of temporomandibular joint (TMJ) masses include synovial chondromatosis, pigmented villonodular synovitis, and sarcomas. Cases of NF in the TMJ are extremely rare, and only 3 cases have been reported to date. Due to its destructive features and rarity, NF has often been misdiagnosed as a more aggressive lesion, which could expose patients to unnecessary and invasive treatment approaches beyond repair. This report presents a case of NF in the TMJ, focusing on various imaging features, along with a literature review aiming to determine the hallmark features of NF in the TMJ and highlight the diagnostic challenges.

Fibrodysplasia ossificans progressiva is a rare hereditary disorder characterized by progressive heterotopic ossifica-tion in muscle and connective tissue, with few reported cases affecting the head and neck region. Although plain radiographic findings and computed tomography features have been well documented, limited reports exist onmagnetic resonance findings. This report presents 2 cases of fibrodysplasia ossificans progressiva, one with limited mouth opening due to heterotopic ossification of the lateral pterygoid muscle and the other with restricted neck movement due to heterotopic ossification of the platysma muscle. Clinical findings of restricted mouth opening or limited neck movement, along with radiological findings of associated heterotopic ossification, should prompt consideration of fibrodysplasia ossificans progressiva in the differential diagnosis. Dentists should be particularly vigilant with patients diagnosed with fibrodysplasia ossificans progressiva to avoid exposure to diagnostic biopsy andinvasive dental procedures.

Purpose: The aim of this study was to evaluate the risk factors associated with glaucoma or ocular hypertension (OHT) in patients taking oral corticosteroids for extended periods, and to aid in managing intraocular pressure (IOP) in patients with these risk factors. Methods: A cross-sectional study was performed involving 690 patients who visited a tertiary referral hospital and had been using oral corticosteroids for more than six months. Patients' demographics, tonometry results, drug type, dosage, duration, ophthalmic history, and the use of glaucoma eye drops were analyzed to determine the risk factors associated with glaucoma or OHT. Results: In a generalized linear model analysis comparing patients' eyes diagnosed with glaucoma or ocular hypertension to those without such diagnoses, no statistical difference was observed between the two groups in terms of drug type, age, and duration of oral corticosteroid use. However, the dosage was found to be statistically significant (odds ratio 1.09, p = 0.0294). Conclusions No difference in the incidence of glaucoma or OHT was found based on the type of oral steroid, age, or duration of use. However, a higher incidence of glaucoma and OHT was observed among patients taking higher doses of oral steroids. Therefore, it is suggested that using lower doses of oral steroids may be more beneficial for managing IOP.

Purpose: Convolutional neural networks (CNNs) have rapidly emerged as one of the most promising artificial intelligence methods in the field of medical and dental research. CNNs can provide an effective diagnostic methodology allowing for the detection of early-staged diseases. Therefore, this study aimed to evaluate the performance of a deep CNN algorithm for apical lesion segmentation from panoramic radiographs. Materials and Methods: A total of 1000 panoramic images showing apical lesions were separated into training (n=800, 80%), validation (n=100, 10%), and test (n=100, 10%) datasets. The performance of identifying apical lesions was evaluated by calculating the precision, recall, and F1-score. Results: In the test group of 180 apical lesions, 147 lesions were segmented from panoramic radiographs with an intersection over union (IoU) threshold of 0.3. The F1-score values, as a measure of performance, were 0.828, 0.815, and 0.742, respectively, with IoU thresholds of 0.3, 0.4, and 0.5. Conclusion This study showed the potential utility of a deep learning-guided approach for the segmentation of apical lesions. The deep CNN algorithm using U-Net demonstrated considerably high performance in detecting apical lesions.

Background: Insulin resistance (IR) and inflammation are closely related to each other and share common pathophysiological and metabolic mechanisms. We aimed to investigate the combined effect of IR and inflammation on comorbidities of type 2 diabetes mellitus (T2DM). Methods: A total 3,758 patients with T2DM were recruited through Huh’s Diabetes Center from January 2003 to June 2009. Insulin sensitivity was measured by a rate constant for plasma glucose disappearance (Kitt , %/min) using short insulin tolerance test. High sensitivity C-reactive protein (hs-CRP) was used as a surrogate for inflammation. Results: Patients with the lowest tertile of Kitt (IR group) showed worse cardio-metabolic parameters while those with the highest tertile of hs-CRP levels had worse cardio-metabolic parameters. The prevalence of metabolic syndrome, fatty liver, albuminuria, and carotid atherosclerosis decreased with Kitt tertile, but increased with hs-CRP tertile. In multiple regression analysis, both Kitt and hs-CRP were independent risk factors for comorbidities of T2DM. In addition, they showed synergistic effects on these comorbidities. Conclusion Both IR and inflammation were significantly associated with comorbidities of T2DM in a dose dependent manner. In addition, the coexistence of IR and inflammation may synergistically contribute to increased comorbidities of T2DM.

Background/Aims: The quick Sepsis-related Organ Failure Assessment (qSOFA) is a newly developed risk stratification tool, which has been presented along with a new sepsis definition, to classify infected patients outside of the intensive care unit (ICU). We evaluated the clinical usefulness of qSOFA for predicting adverse outcomes in sepsis patients with liver cirrhosis. Methods: We performed a retrospective cohort study to assess the utility of qSOFA in sepsis patients with liver cirrhosis for whom medical emergency teams (METs) were activated in general wards at an academic tertiary care hospital between March 2008 and December 2015. qSOFA, Systemic inflammatory response syndrome (SIRS), modified early warning score (MEWS), and sequential (sepsis- related) organ failure assessment (SOFA) scores were calculated according to data at MET activation. Results: Of 188 patients, 69 (36.7%) had a qSOFA score of 0 or 1 point and 119 (63.3%) had ≥ 2 points. The areas under the receiver operating characteristic curve (AUROC) for ICU transfer on the SOFA (AUROC, 0.691; 95% confidence interval [CI], 0.615 to 0.767) or MEWS (AUROC, 0.663; 95% CI, 0.586 to 0.739) were significantly higher compared to those for qSOFA (AUROC, 0.589; 95% CI, 0.507 to 0.671) or SIRS (AUROC, 0.533; 95% CI, 0.451 to 0.616). Conclusions Our findings suggest that qSOFA score may have limited utility in predicting adverse outcomes in sepsis patients with liver cirrhosis at MET activation. Either MEWS or another screening tool is needed for detecting early sepsis in these patients.

Background/Aims: Scoring systems play an important role in predicting intensive care unit (ICU) admission or estimating the risk of death in critically ill patients with hematological malignancies. We evaluated the modified early warning score (MEWS) for predicting ICU admissions and in-hospital mortality among at-risk patients with hematological malignancies and developed an optimized MEWS. Methods: We retrospectively analyzed derivation cohort patients with hematological malignancies who were managed by a medical emergency team (MET) in the general ward and prospectively validated the data. We compared the traditional MEWS with the MEWS plus SpO2/FiO2 (MEWS_SF) score, which were calculated at the time of MET contact. Results: In the derivation cohort, the areas under the receiver-operating characteristic (AUROC) curves were 0.81 for the MEWS (95% confidence interval [CI], 0.76 to 0.87) and 0.87 for the MEWS_SF score (95% CI, 0.87 to 0.92) for predicting ICU admission. The AUROC curves were 0.70 for the MEWS (95% CI, 0.63 to 0.77) and 0.76 for the MEWS_SF score (95% CI, 0.70 to 0.83) for predicting in-hospital mortality. In the validation cohort, the AUROC curves were 0.71 for the MEWS (95% CI, 0.66 to 0.77) and 0.83 for the MEWS_SF score (95% CI, 0.78 to 0.87) for predicting ICU admission. The AUROC curves were 0.64 for the MEWS (95% CI, 0.57 to 0.70) and 0.74 for the MEWS_SF score (95% CI, 0.69 to 0.80) for predicting in-hospital mortality. Conclusions Compared to the traditional MEWS, the MEWS_SF score may be a useful tool that can be used in the general ward to identify deteriorating patients with hematological malignancies.