Diagnosis of Tooth Prognosis Using Artificial Intelligence.

The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1-Hopeless, 5-Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan.

Professional leadership training programs for dental faculty: Perspectives of the US dental school deans.

INTRODUCTION: The purpose of the study was to explore the influence of leadership training programs on career development and to gain insight into the perspectives on faculty training programs according to the current US dental school deans. METHODS: This prospective study analyzed the results of a questionnaire distributed to 67 active Deans of U.S dental schools using a survey instrument that explored the areas of leadership programs that were useful, the support mechanisms received from institutions, and the necessity to integrate formal leadership development programs into training programs for faculty and students. RESULTS: Among 33 participants (49.3% participation rate) in the study, 97% of responders strongly or somewhat agreed that leadership development programs could improve the quality of leadership and administrative skills. The respondents found that the most useful areas in leadership training were conflict resolution, communication skills, organizational change, diversity, inclusion, and belonging. The findings highlighted that critical barriers to accessing leadership training are financial support and lack of protected time for faculty. The results also showed that 73% strongly or somewhat agreed that the leadership training should be integrated into the predoctoral program and 87% into the postdoctoral graduate programs. CONCLUSION: Participation in formal leadership development programs for faculty leaders in dental education is viewed as beneficial and could offer dental educators leadership training to better navigate the academic environment. The findings suggest the need to further explore the availability and effectiveness of leadership development opportunities for developing current and future educators and administrative leaders.

Risk Evaluation of Azithromycin-Induced QT Prolongation in Real-World Practice.

BACKGROUND: Azithromycin exposure has been reported to increase the risk of QT prolongation and cardiovascular death. However, findings on the association between azithromycin and cardiovascular death are controversial, and azithromycin is still used in actual practice. Additionally, quantitative assessments of risk have not been performed, including the risk of QT prolongation when patients are exposed to azithromycin in a real-world clinical setting. Therefore, in this study, we aimed to evaluate the risk of exposure to azithromycin on QT prolongation in a real-world clinical setting using a 21-year medical history database of a tertiary medical institution. METHODS: We analyzed the electrocardiogram results and relevant electronic health records of 402,607 subjects in a tertiary teaching hospital in Korea from 1996 to 2015. To evaluate the risk of QT prolongation of azithromycin, we conducted a case-control analysis using amoxicillin for comparison. Multiple logistic regression analysis was performed to correct for age, sex, accompanying drugs, and disease. RESULTS: The odds ratio (OR) for QT prolongation (QTc>450 ms in male and >460 ms in female) on azithromycin exposure was 1.40 (95% confidence interval [CI], 1.23-1.59), and the OR for severe QT prolongation (QTc>500 ms) was 1.43 (95% CI, 1.13-1.82). On the other hand, the ORs on exposure to amoxicillin were 1.06 (95% CI, 0.97-1.15) and 0.88 (95% CI, 0.70-1.09). In a subgroup analysis, the risk of QT prolongation in patients aged between 60 and 80 years was significantly higher when they are exposed to azithromycin. CONCLUSIONS: The risk of QT prolongation was increased when patients, particularly the elderly aged 60-79 years, were exposed to azithromycin. Therefore, clinicians should pay exercise caution using azithromycin or consider using other antibiotics, such as amoxicillin, instead of azithromycin.

Construction of an Electrocardiogram Database Including 12 Lead Waveforms.

OBJECTIVES: Electrocardiogram (ECG) data are important for the study of cardiovascular disease and adverse drug reactions. Although the development of analytical techniques such as machine learning has improved our ability to extract useful information from ECGs, there is a lack of easily available ECG data for research purposes. We previously published an article on a database of ECG parameters and related clinical data (ECG-ViEW), which we have now updated with additional 12-lead waveform information. METHODS: All ECGs stored in portable document format (PDF) were collected from a tertiary teaching hospital in Korea over a 23-year study period. We developed software which can extract all ECG parameters and waveform information from the ECG reports in PDF format and stored it in a database (meta data) and a text file (raw waveform). RESULTS: Our database includes all parameters (ventricular rate, PR interval, QRS duration, QT/QTc interval, P-R-T axes, and interpretations) and 12-lead waveforms (for leads I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6) from 1,039,550 ECGs (from 447,445 patients). Demographics, drug exposure data, diagnosis history, and laboratory test results (serum calcium, magnesium, and potassium levels) were also extracted from electronic medical records and linked to the ECG information. CONCLUSIONS: Electrocardiogram information that includes 12 lead waveforms was extracted and transformed into a form that can be analyzed. The description and programming codes in this case report could be a reference for other researchers to build ECG databases using their own local ECG repository.

Managing uncertainties in the surgical scheduling.

Current surgical scheduling system has difficulties to handle unpredictable events or uncertainties. Source of uncertainties may come from the patient or the surgery itself, where several cases require immediate changes in data, such as when surgery delays or cancellation occurs on the same day. The study aimed to model the uncertainties for managing identified uncertainties during the continuous scheduling, framed by resilience concept to cope with the system fragility. In order to be able to control and adjust any changes which may affect the surgery schedule of the day, we provide alternatives of solution rather than strictly decide the best valued options. We identified dimensions of uncertainties and categorized them based on the resilience concept, computed the impact value of potentially conflicted resources as a result of schedule change. With the model applied, we would provide a list of most acceptable and less vulnerable alternatives for anesthesiologist as a scheduler to build resilience in the surgical scheduling.

The antihypertensive effect of ethyl acetate extract of radish leaves in spontaneously hypertensive rats.

Radish (Raphanus sativus L.) is a cruciferous vegetable, and its leaves have antioxidant and anticancer properties. This study was conducted to evaluate the effects of ethyl acetate extracts from radish leaves on hypertension in 11-week-old spontaneously hypertensive rats (SHRs). The SHRs were randomly divided into 3 groups of 6 rats each on the basis of initial systolic blood pressure (SBP) and were treated with oral administration of radish leaf extract (0, 30, or 90 mg/kg body weight [bw], respectively) for 5 weeks. Six Wistar rats were used as normotensive controls. The amount of the radish leaf extract had no effect on body weight. The SBP of the SHRs showed a decreasing trend with the consumption of the radish leaf extract. In the third week, the SBP of the group fed 90 mg extract/kg bw reduced from 214 mmHg to 166 mmHg and was significantly lower than that of the normotensive and hypertensive controls. The extract did not show a significant effect on the angiotensin-converting enzyme (ACE) activity in the serum, kidney, and lung. The extract increased the concentration of NO in serum and the activities of antioxidant enzymes such as glutathione peroxidase and catalase in red blood cells (RBCs). The serum concentrations of Na(+) and K(+) were not significantly different between all groups. However, the fecal concentrations of Na(+) and K(+) increased; the fecal concentrations of Na(+) and K(+) for the normotensive and hypertensive controls were not different. Urinary excretion of Na(+) was higher in the normotensive Wistar rats than in the SHRs, while that of K(+) was not significantly different. These findings indicate that consumption of radish leaves might have had antihypertensive effects in SHRs by increasing the serum concentration of NO and fecal concentration of Na(+) and enhancing antioxidant activities.