METHOD: To overcome the limitation, the use of artificial intelligence along with technical tools has been extensively investigated for AD diagnosis. For developing a promising artificial intelligence strategy that can diagnose AD early, it is critical to supervise neuropsychological outcomes and imaging-based readouts with a proper clinical review.

METHOD: Two large datasets, including 1110 3D CT images, were split into five segments of 20% each. Each dataset's first 20% segment was separated as a holdout test set. 3D-CNN training was performed with the remaining 80% from each dataset. Two small external datasets were also used to independently evaluate the trained models.

RESULTS: The total combination of 80% of each dataset has an accuracy of 91% on Iranmehr and 83% on Moscow holdout test datasets. Results indicated that 80% of the primary datasets are adequate for fully training a model. The additional fine-tuning using 40% of a secondary dataset helps the model generalize to a third, unseen dataset. The highest accuracy achieved through transfer learning was 85% on LDCT dataset and 83% on Iranmehr holdout test sets when retrained on 80% of Iranmehr dataset.

OBJECTIVE: To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score.

METHODS: The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score.

RESULTS: A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation.

AREAS COVERED: This review will highlight dengue diagnostics strategies and discuss other possible targets for dengue diagnosis. Understanding the dynamics of the immune response and how it affects viral infection has enabled informed diagnosis. As more technologies emerge, precise assays that include some clinical markers need to be included.

METHODS: The study included 382 participants (252 normal voices and 130 dysphonic voices) in the proposed database MVPD. Complete data were obtained for both groups, including voice samples, laryngostroboscopy videos, and acoustic analysis. The diagnoses of patients with dysphonia were obtained. Each voice sample was anonymized using a code that was specific to each individual and stored in the MVPD. These voice samples were used to train and test the proposed OSELM algorithm. The performance of OSELM was evaluated and compared with other classifiers in terms of the accuracy, sensitivity, and specificity of detecting and differentiating dysphonic voices.

RESULTS: The accuracy, sensitivity, and specificity of OSELM in detecting normal and dysphonic voices were 90%, 98%, and 73%, respectively. The classifier differentiated between structural and non-structural vocal fold pathology with accuracy, sensitivity, and specificity of 84%, 89%, and 88%, respectively, while it differentiated between malignant and benign lesions with an accuracy, sensitivity, and specificity of 92%, 100%, and 58%, respectively. Compared to other classifiers, OSELM showed superior accuracy and sensitivity in detecting dysphonic voices, differentiating structural versus non-structural vocal fold pathology, and between malignant and benign voice pathology.

METHODS: Eighteen students with prior experience in traditional PDPBL processes participated in the study, divided into three groups to perform PDPBL sessions with various triggers from pharmaceutical chemistry, pharmaceutics, and clinical pharmacy fields, while utilizing chat AI provided by ChatGPT to assist with data searching and problem-solving. Questionnaires were used to collect data on the impact of ChatGPT on students' satisfaction, engagement, participation, and learning experience during the PBL sessions.

RESULTS: The survey revealed that ChatGPT improved group collaboration and engagement during PDPBL, while increasing motivation and encouraging more questions. Nevertheless, some students encountered difficulties understanding ChatGPT's information and questioned its reliability and credibility. Despite these challenges, most students saw ChatGPT's potential to eventually replace traditional information-seeking methods.

METHODS: External root resorption was simulated on 88 extracted premolar teeth using tungsten bur in different depths (0.5 mm, 1 mm, and 2 mm). All teeth were scanned using a Cone beam CT (Carestream Dental, Atlanta, GA). Afterward, a training (70%), validation (10%), and test (20%) dataset were established. The performance of four DLMs including Random Forest (RF) + Visual Geometry Group 16 (VGG), RF + EfficienNetB4 (EFNET), Support Vector Machine (SVM) + VGG, and SVM + EFNET) and four hybrid models (DLM + FST: (i) FS + RF + VGG, (ii) FS + RF + EFNET, (iii) FS + SVM + VGG and (iv) FS + SVM + EFNET) was compared. Five performance parameters were assessed: classification accuracy, F1-score, precision, specificity, and error rate. FST algorithms (Boruta and Recursive Feature Selection) were combined with the DLMs to assess their performance.

RESULTS: RF + VGG exhibited the highest performance in identifying ERR, followed by the other tested models. Similarly, FST combined with RF + VGG outperformed other models with classification accuracy, F1-score, precision, and specificity of 81.9%, weighted accuracy of 83%, and area under the curve (AUC) of 96%. Kruskal Wallis test revealed a significant difference (p = 0.008) in the prediction accuracy among the eight DLMs.