Fusion extracted features from deep learning for identification of multiple positioning errors in dental panoramic imaging.

BACKGROUND: Dental panoramic imaging plays a pivotal role in dentistry for diagnosis and treatment planning. However, correctly positioning patients can be challenging for technicians due to the complexity of the imaging equipment and variations in patient anatomy, leading to positioning errors. These errors can compromise image quality and potentially result in misdiagnoses. OBJECTIVE: This research aims to develop and validate a deep learning model capable of accurately and efficiently identifying multiple positioning errors in dental panoramic imaging. METHODS AND MATERIALS: This retrospective study used 552 panoramic images selected from a hospital Picture Archiving and Communication System (PACS). We defined six types of errors (E1-E6) namely, (1) slumped position, (2) chin tipped low, (3) open lip, (4) head turned to one side, (5) head tilted to one side, and (6) tongue against the palate. First, six Convolutional Neural Network (CNN) models were employed to extract image features, which were then fused using transfer learning. Next, a Support Vector Machine (SVM) was applied to create a classifier for multiple positioning errors, using the fused image features. Finally, the classifier performance was evaluated using 3 indices of precision, recall rate, and accuracy. RESULTS: Experimental results show that the fusion of image features with six binary SVM classifiers yielded high accuracy, recall rates, and precision. Specifically, the classifier achieved an accuracy of 0.832 for identifying multiple positioning errors. CONCLUSIONS: This study demonstrates that six SVM classifiers effectively identify multiple positioning errors in dental panoramic imaging. The fusion of extracted image features and the employment of SVM classifiers improve diagnostic precision, suggesting potential enhancements in dental imaging efficiency and diagnostic accuracy. Future research should consider larger datasets and explore real-time clinical application.

Vitex rotundifolia L. f. and Vitex trifolia L.: A review on their traditional medicine, phytochemistry, pharmacology.

ETHNOPHARMACOLOGICAL RELEVANCE: Vitex rotundifolia L. f. and Vitex trifolia L. belong to the genus Vitex, and Vitex rotundifolia L. f. evolved from Vitex trifolia L. Both are essential ethnic medicinal plants with a long history, commonly used to treat headaches, fever, diarrhea, hair loss, wound recovery, and other diseases. AIM OF THE REVIEW: The research status of Vitex trifolia L. and its relative species Vitex rotundifolia L. f. were reviewed from the aspects of traditional medicinal use, phytochemistry, and pharmacological activities, to provide a reference for the further development and utilization of Vitex rotundifolia L. f. and Vitex trifolia L. MATERIALS AND METHODS: In this paper, a comprehensive search of published literature was conducted through various books and online databases to obtain relevant information on Vitex rotundifolia L. f. and Vitex trifolia L. The search terms "(Vitex rotundifolia) OR (Vitex trifolia) OR (Fructus viticis)" were entered in PubMed, Web of Science, China national knowledge infrastructure (CNKI), Wanfang Data, Baidu Scholar, respectively. In addition to setting the year threshold of "2018-2022" on Baidu Scholar, other databases searched all fields and found 889, 283, 1263, 1023, and 147 articles, respectively. Among them, review, repetition, overlapping data, and other reasons were excluded, and finally, a total of 164 articles were included in the review study. RESULTS: A total of 369 compounds have been identified, including 159 terpenoids, 51 flavonoids, 83 phenylpropanoids, and 76 other compounds. Pharmacological studies have shown that Vitex rotundifolia L. f. and Vitex trifolia L. have a variety of pharmacological activities, such as anti-tumor, analgesic, antipyretic, anti-inflammatory, antioxidant, antibacterial, and estrogen-like activity. Modern clinical use for treating cold headaches, diarrhea dysentery, irregular menstruation, and other diseases. CONCLUSIONS: As traditional medicinal plants, Vitex rotundifolia L. f. and Vitex trifolia L. have wealthy chemical constituents and extensive pharmacological activities and are widely used in clinical practice from traditional to modern times. However, the research on the pharmacological activities of Vitex rotundifolia L. f. and Vitex trifolia L. is not in-depth, and the potential active components still need to be explored.

Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated.

Traditional Chinese medicines (TCMs), as a unique natural medicine resource, were used to prevent and treat bacterial diseases in China with a long history. To provide a prediction model of screening antibacterial TCMs for the design and discovery of novel antibacterial agents, the literature about antibacterial TCMs in the China National Knowledge Infrastructure (CNKI) and Web of Science database was retrieved. The data were extracted and standardized. A total of 28,786 pieces of data from 904 antibacterial TCMs were collected. The data of plant medicine were the most numerous. The result of association rules mining showed a high correlation between antibacterial activity with cold nature, bitter and sour tastes, hemostatic, and purging fire efficacies. Moreover, TCMs with antibacterial activity showed a specific aggregation in the phylogenetic tree; 92% of them came from Tracheophyta, of which 74% were mainly concentrated in rosids, asterids, Liliopsida, and Ranunculales. The prediction models of anti-Escherichia coli and anti-Staphylococcus aureus activity, with AUC values (the area under the ROC curve) of 77.5 and 80.0%, respectively, were constructed by the Neural Networks (NN) algorithm after Bagged Classification and Regression Tree (Bagged CART) and Linear Discriminant Analysis (LDA) selection. The in vitro experimental results showed the prediction accuracy of these two models was 75 and 60%, respectively. Four TCMs (Cirsii Japonici Herba Carbonisata, Changii Radix, Swertiae Herba, Callicarpae Formosanae Folium) were proposed for the first time to show antibacterial activity against E. coli and/or S. aureus. The results implied that the prediction model of antibacterial activity of TCMs based on properties and families showed certain prediction ability, which was of great significance to the screening of antibacterial TCMs and can be used to discover novel antibacterial agents.