Artificial intelligence in diagnosing dens evaginatus on periapical radiography with limited data availability.

This study aimed to develop an artificial intelligence (AI) model using deep learning techniques to diagnose dens evaginatus (DE) on periapical radiography (PA) and compare its performance with endodontist evaluations. In total, 402 PA images (138 DE and 264 normal cases) were used. A pre-trained ResNet model, which had the highest AUC of 0.878, was selected due to the small number of data. The PA images were handled in both the full (F model) and cropped (C model) models. There were no significant statistical differences between the C and F model in AI, while there were in endodontists (p = 0.753 and 0.04 in AUC, respectively). The AI model exhibited superior AUC in both the F and C models compared to endodontists. Cohen's kappa demonstrated a substantial level of agreement for the AI model (0.774 in the F model and 0.684 in C) and fair agreement for specialists. The AI's judgment was also based on the coronal pulp area on full PA, as shown by the class activation map. Therefore, these findings suggest that the AI model can improve diagnostic accuracy and support clinicians in diagnosing DE on PA, improving the long-term prognosis of the tooth.

Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography.

Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen's kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment.

Synthesis and photovoltaic properties of cyclopentadithiophene-based low-bandgap copolymers that contain electron-withdrawing thiazole derivatives.

We have synthesized four types of cyclopentadithiophene (CDT)-based low-bandgap copolymers, poly[{4,4-bis(2-ethylhexyl)-4H-cyclopenta[2,1-b:3,4-b']dithiophene-2,6-diyl}-alt-(2,2'-bithiazole-5,5'-diyl)] (PehCDT-BT), poly[(4,4-dioctyl-4H-cyclopenta[2,1-b:3,4-b']dithiophene-2,6-diyl)-alt-(2,2'-bithiazole-5,5'-diyl)] (PocCDT-BT), poly[{4,4-bis(2-ethylhexyl)-4H-cyclopenta[2,1-b:3,4-b']dithiophene-2,6-diyl}-alt-{2,5-di(thiophen-2-yl)thiazolo[5,4-d]thiazole-5,5'-diyl}] (PehCDT-TZ), and poly[(4,4-dioctyl-4H-cyclopenta[2,1-b:3,4-b']dithiophene-2,6-diyl)-alt-{2,5-di(thiophen-2-yl)thiazolo[5,4-d]thiazole-5,5'-diyl}] (PocCDT-TZ), for use in photovoltaic applications. The intramolecular charge-transfer interaction between the electron-sufficient CDT unit and electron-deficient bithiazole (BT) or thiazolothiazole (TZ) units in the polymeric backbone induced a low bandgap and broad absorption that covered 300 nm to 700-800 nm. The optical bandgap was measured to be around 1.9 eV for PehCDT-BT and PocCDT-BT, and around 1.8 eV for PehCDT-TZ and PocCDT-TZ. Gel permeation chromatography showed that number-average molecular weights ranged from 8000 to 14,000 g mol(-1). Field-effect mobility measurements showed hole mobility of 10(-6)-10(-4) cm(2) V(-1) s(-1) for the copolymers. The film morphology of the bulk heterojunction mixtures with [6,6]phenyl-C(61)-butyric acid methyl ester (PCBM) was also examined by atomic force microscopy before and after heat treatment. When the polymers were blended with PCBM, PehCDT-TZ exhibited the best performance with an open circuit voltage of 0.69 V, short-circuit current of 7.14 mA cm(-2), and power conversion efficiency of 2.23 % under air mass (AM) 1.5 global (1.5 G) illumination conditions (100 mW cm(-2)).