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Purpose: The aims of this study were to create a deep learning model to distinguish between nasopalatine duct cysts (NDCs), radicular cysts, and no-lesions (normal) in the midline region of the anterior maxilla on panoramic radiographs and to compare its performance with that of dental residents. Materials and Methods: One hundred patients with a confirmed diagnosis of NDC (53 men, 47 women; average age, 44.6±16.5 years), 100 with radicular cysts (49 men, 51 women; average age, 47.5±16.4 years), and 100 with normal groups (56 men, 44 women; average age, 34.4±14.6 years) were enrolled in this study. Cases were randomly assigned to the training datasets (80%) and the test dataset (20%). Then, 20% of the training data were randomly assigned as validation data. A learning model was created using a customized DetectNet built in Digits version 5.0 (NVIDIA, Santa Clara, USA). The performance of the deep learning system was assessed and compared with that of two dental residents. Results: The performance of the deep learning system was superior to that of the dental residents except for the recall of radicular cysts. The areas under the curve (AUCs) for NDCs and radicular cysts in the deep learning system were significantly higher than those of the dental residents. The results for the dental residents revealed a significant difference in AUC between NDCs and normal groups. Conclusion: This study showed superior performance in detecting NDCs and radicular cysts and in distinguishing between these lesions and normal groups. [ABSTRACT FROM AUTHOR]

The identification of teeth in 3D medical images can be a first step for victim identification from scant remains, for comparison of ante- and postmortem images or for other forensic investigations. We evaluate the performance of a tooth detection approach on mandibles with missing parts or pathologies based on statistical shape models. The proposed approach relies on a shape model that has been built from the full lower jaw, including the mandible and teeth. The model is fitted to the target, resulting in a reconstruction, in addition to a label map that indicates the presence or absence of teeth. We evaluate the accuracy of the proposed solution on a dataset consisting of 76 target mandibles, all extracted from CT images and exhibiting various cases of missing teeth or other cases, such as roots, implants, first dentition, and gap closure. We show an accuracy of approximately 90% on the front teeth (including incisors and canines in our study) that decreases for the molars due to high false-positive rates at the wisdom teeth level. Despite the drop in performance, the proposed approach can be used to obtain an estimate of the tooth count without wisdom teeth, tooth identification, reconstruction of the existing teeth to automate measurements taken as part of routine forensic procedures, or prediction of the missing teeth shape. In comparison to other approaches, our solution relies solely on shape information. This means it can be applied to cases obtained from either medical images or 3D scans because it does not depend on the imaging modality intensities. Another novelty is that the proposed solution avoids heuristics for the separation of teeth or for fitting individual tooth models. The solution is therefore not target-specific and can be directly applied to detect missing parts in other target organs using a shape model of the new target. [ABSTRACT FROM AUTHOR]

Purpose: The diagnostic criteria for osteoporosis are based on the bone mineral density (BMD) level in the lumbar spine and femur bone. Patients with osteoporotic fractures were diagnosed with osteoporosis. While systemic BMD and mandibular cortical bone morphology are correlated, this has not been studied in patients with a history of osteoporotic fractures. Therefore, purpose of this study was researching the mandibular cortical bone morphology in patients with osteoporotic fractures., Methods: The subjects were 55 female and 20 male patients with osteoporotic fractures. Patients were divided into 30 primary osteoporosis patients and 45 secondary osteoporosis patients according to the medical history. Patients underwent BMD and panoramic radiography examinations during orthopedic treatment for fractures. A dual-energy X-ray absorptiometry system was used to measure BMD. Mandibular cortex index (MCI) and mandibular cortex width (MCW) were evaluated using machine-learning measurement software., Results: In the analysis of MCI, the ratio of class 2 and 3 was 73% of both primary osteoporosis and secondary osteoporosis. The average MCW was 2.19 mm for primary osteoporosis and 2.30 mm for secondary osteoporosis. The sensitivity values by MCI and MCW were 73% and 76% for both primary and secondary osteoporosis, which were similar detection powers. In addition, the false-negative rates by MCI and MCW were 27% and 24%., Conclusion: We suggested that MCI and MCW are indicators of osteoporotic conditions in patients with primary and secondary osteoporosis. Our results show that MCI and MCW are non-inferior to the sensitivity values for lumbar BMD in patients with osteoporotic fractures., (© 2022. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

Objectives: Novel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR., Materials and Methods: A narrative review was conducted on the literature on AI algorithms in DMFR., Results: In the field of DMFR, AI algorithms were mainly proposed for (1) automated detection of dental caries, periapical pathologies, root fracture, periodontal/peri-implant bone loss, and maxillofacial cysts/tumors; (2) classification of mandibular third molars, skeletal malocclusion, and dental implant systems; (3) localization of cephalometric landmarks; and (4) improvement of image quality. Data insufficiency, overfitting, and the lack of interpretability are the main issues in the development and use of image-based AI algorithms. Several strategies have been suggested to address these issues, such as data augmentation, transfer learning, semi-supervised training, few-shot learning, and gradient-weighted class activation mapping., Conclusions: Further integration of relevant AI algorithms into one fully automatic end-to-end intelligent system for possible multi-disciplinary applications is very likely to be a field of increased interest in the future., Clinical Relevance: This review provides dental practitioners and researchers with a comprehensive understanding of the current development, performance, issues, and prospects of image-based AI algorithms in DMFR., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: The goal of this study was to develop and evaluate the performance of a new deep-learning (DL) artificial intelligence (AI) model for diagnostic charting in panoramic radiography., Methods: One thousand eighty-four anonymous dental panoramic radiographs were labeled by two dento-maxillofacial radiologists for ten different dental situations: crown, pontic, root-canal treated tooth, implant, implant-supported crown, impacted tooth, residual root, filling, caries, and dental calculus. AI Model CranioCatch, developed in Eskişehir, Turkey and based on a deep CNN method, was proposed to be evaluated. A Faster R-CNN Inception v2 (COCO) model implemented with the TensorFlow library was used for model development. The assessment of AI model performance was evaluated with sensitivity, precision, and F1 scores., Results: When the performance of the proposed AI model for detecting dental conditions in panoramic radiographs was evaluated, the best sensitivity values were obtained from the crown, implant, and impacted tooth as 0.9674, 0.9615, and 0.9658, respectively. The worst sensitivity values were obtained from the pontic, caries, and dental calculus, as 0.7738, 0.3026, and 0.0934, respectively. The best precision values were obtained from pontic, implant, implant-supported crown as 0.8783, 0.9259, and 0.8947, respectively. The worst precision values were obtained from residual root, caries, and dental calculus, as 0.6764, 0.5096, and 0.1923, respectively. The most successful F1 Scores were obtained from the implant, crown, and implant-supported crown, as 0.9433, 0.9122, and 0.8947, respectively., Conclusion: The proposed AI model has promising results at detecting dental conditions in panoramic radiographs, except for caries and dental calculus. Thanks to the improvement of AI models in all areas of dental radiology, we predict that they will help physicians in panoramic diagnosis and treatment planning, as well as digital-based student education, especially during the pandemic period., (© 2021. The Author(s), under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology and Springer Nature Singapore Pte Ltd.)

This study aimed to propose a computerized method for detecting the tooth region for each tooth type as the initial stage in the development of a computer-aided diagnosis (CAD) scheme for dental panoramic X-ray images. Our database consists of 160 panoramic dental X-ray images obtained from 160 adult patients. To reduce false positives (FPs), the proposed method first extracts a rectangular area including all teeth from a dental panoramic X-ray image with a faster region using a convolutional neural network (Faster R-CNN). From the rectangular area including all teeth, six divided areas are then extracted with Faster R-CNN: top left, top center, top right, bottom left, bottom center, and bottom right. Faster R-CNNs for detecting tooth regions for each tooth type were trained individually for each of the divided areas that narrowed down the target tooth types. By applying these Faster R-CNNs to each divided area, the bounding boxes of each tooth were detected and classified into 32 tooth types. A k-fold cross-validation method with k = 4 was used for training and testing the proposed method. The detection rate for each tooth, number of FPs per image, mean intersection over union for each tooth, and classification accuracy for the 32 tooth types were 98.9%, 0.415, 0.748, and 91.7%, respectively, showing an improvement compared to the application of the Faster R-CNN once to the entire image (98.0%, 1.194, 0.736, and 88.8%)., (© 2022. The Author(s), under exclusive licence to Japanese Society of Radiological Technology and Japan Society of Medical Physics.)

The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures; (B) explain the workflow for developing AI solutions, from data collection through clinical integration; (C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry., (© 2024 British Endodontic Society. Published by John Wiley & Sons Ltd.)

Objectives: Accurately identification and tooth numbering on radiographs is essential for any clinicians. The aim of the present study was to validate the hypothesis that Yolov5, a type of artificial intelligence model, can be trained to detect and number teeth in periapical radiographs., Materials and Methods: Six thousand four hundred forty six anonymized periapical radiographs without motion-related artifacts were randomly selected from the database. All periapical radiographs in which all boundaries of any tooth could be distinguished were included in the study. The radiographic images used were randomly divided into three groups: 80% training, 10% validation, and 10% testing. The confusion matrix was used to examine model success., Results: During the test phase, 2578 labelings were performed on 644 periapical radiographs. The number of true positive was 2434 (94.4%), false positive was 115 (4.4%), and false negative was 29 (1.2%). The recall, precision, and F1 scores were 0.9882, 0.9548, and 0.9712, respectively. Moreover, the model yielded an area under curve (AUC) of 0.603 on the receiver operating characteristic curve (ROC)., Conclusions: This study showed us that YOLOv5 is nearly perfect for numbering teeth on periapical radiography. Although high success rates were achieved as a result of the study, it should not be forgotten that artificial intelligence currently only can be guides dentists for accurate and rapid diagnosis., Clinical Relevance: It is thought that dentists can accelerate the radiographic examination time and inexperienced dentists can reduce the error rate by using YOLOv5. Additionally, YOLOv5 can also be used in the education of dentistry students., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). Materials and methods: An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. Results: Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3–0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. Conclusion: AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. Protocol: PROSPERO identifier: CRD42023470708 [ABSTRACT FROM AUTHOR]

Idiopathic osteosclerosis (IO) are focal radiopacities of unknown etiology observed in the jaws. These radiopacities are incidentally detected on dental panoramic radiographs taken for other reasons. In this study, we investigated the performance of a deep learning model in detecting IO using a small dataset of dental panoramic radiographs with varying contrasts and features. Two radiologists collected 175 IO-diagnosed dental panoramic radiographs from the dental school database. The dataset size is limited due to the rarity of IO, with its incidence in the Turkish population reported as 2.7% in studies. To overcome this limitation, data augmentation was performed by horizontally flipping the images, resulting in an augmented dataset of 350 panoramic radiographs. The images were annotated by two radiologists and divided into approximately 70% for training (245 radiographs), 15% for validation (53 radiographs), and 15% for testing (52 radiographs). The study employing the YOLOv5 deep learning model evaluated the results using precision, recall, F1-score, mAP (mean Average Precision), and average inference time score metrics. The training and testing processes were conducted on the Google Colab Pro virtual machine. The test process's performance criteria were obtained with a precision value of 0.981, a recall value of 0.929, an F1-score value of 0.954, and an average inference time of 25.4 ms. Although radiographs diagnosed with IO have a small dataset and exhibit different contrasts and features, it has been observed that the deep learning model provides high detection speed, accuracy, and localization results. The automatic identification of IO lesions using artificial intelligence algorithms, with high success rates, can contribute to the clinical workflow of dentists by preventing unnecessary biopsy procedure. [ABSTRACT FROM AUTHOR]

Amongst all types of cancer, breast cancer has become one of the most common cancers in the UK threatening millions of people's health. Early detection of breast cancer plays a key role in timely treatment for morbidity reduction. Compared to biopsy, which takes tissues from the lesion for further analysis, image-based methods are less time-consuming and pain-free though they are hampered by lower accuracy due to high false positivity rates. Nevertheless, mammography has become a standard screening method due to its high efficiency and low cost with promising performance. Breast mass, as the most palpable symptom of breast cancer, has received wide attention from the community. As a result, the past decades have witnessed the speeding development of computer-aided systems that are aimed at providing radiologists with useful tools for breast mass analysis based on mammograms. However, the main issues of these systems include low accuracy and require enough computational power on a large scale of datasets. To solve these issues, we developed a novel breast mass classification system called DF-dRVFL. On the public dataset DDSM with more than 3500 images, our best model based on deep random vector functional link network showed promising results through five-cross validation with an averaged AUC of 0.93 and an average accuracy of 81.71 % . Compared to sole deep learning based methods, average accuracy has increased by 0.38. Compared with the state-of-the-art methods, our method showed better performance considering the number of images for evaluation and the overall accuracy. [ABSTRACT FROM AUTHOR]

Deep learning (DL) requires a large amount of training data to improve performance and prevent overfitting. To overcome these difficulties, we need to increase the size of the training dataset. This can be done by augmentation on a small dataset. The augmentation approaches must enhance the model's performance during the learning period. There are several types of transformations that can be applied to medical images. These transformations can be applied to the entire dataset or to a subset of the data, depending on the desired outcome. In this study, we categorize data augmentation methods into four groups: Absent augmentation, where no modifications are made; basic augmentation, which includes brightness and contrast adjustments; intermediate augmentation, encompassing a wider array of transformations like rotation, flipping, and shifting in addition to brightness and contrast adjustments; and advanced augmentation, where all transformation layers are employed. We plan to conduct a comprehensive analysis to determine which group performs best when applied to brain CT images. This evaluation aims to identify the augmentation group that produces the most favorable results in terms of improving model accuracy, minimizing diagnostic errors, and ensuring the robustness of the model in the context of brain CT image analysis., (© 2024. International Federation for Medical and Biological Engineering.)

Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction., (© 2024 Wiley Periodicals LLC.)

Streptococcus suis is a gram-positive bacterium that causes meningitis, septicemia, endocarditis, and other disorders in pigs and humans. We obtained 42 and 50 S. suis isolates from lesions of porcine endocarditis and palatine tonsils, respectively, of clinically healthy pigs in Japan; we then determined their sequence types (STs) by multilocus sequence typing (MLST), cps genotypes, serotypes, and presence of classical major virulence-associated marker genes (mrp, epf, and sly). The 42 isolates from endocarditis lesions were assigned to a limited number of STs and clonal complexes (CCs). On the other hand, the 50 isolates from tonsils were diverse in these traits and seemingly in the degree of virulence, suggesting that tonsils can accommodate a variety of S. suis isolates. The goeBURST full algorithm using tonsil isolates obtained in this study and those retrieved from the database showed that major CCs as well as many other clusters were composed of isolates originating from different countries, and some of the STs were very similar to each other despite the difference in country of origin. These findings indicate that S. suis with not only different but also similar mutations in the genome have survived in tonsils independently across different geographical locations. Therefore, unlike the lesions of endocarditis, the tonsils of pigs seemingly accommodate various S. suis lineages. The present study suggests that S. suis acquired its diversity by natural mutations during colonization and persistence in the tonsils of pigs. [ABSTRACT FROM AUTHOR]

Microaneurysms, tiny, circular red dots that occur in retinal fundus images, are one of the earliest symptoms of diabetic retinopathy. Because microaneurysms are small and delicate, detecting them can be difficult. Their small size and cunning character make automatic detection of them difficult. The automatic detection of microaneurysms in retinal fundus images is proposed in this research using a local Fourier transform and neighbourhood analysis-based multi-scale approach technique. The suggested method is broken down into three stages: image preprocessing, the detection of retinal vessels and microaneurysm candidates, and labelling of the candidates. A multi-scale framework is used to develop every stage of the algorithm, with the exception of the initial image preprocessing, giving the mechanism for efficient microaneurysms detection. In contrast to the short-time Fourier transform, which extracts the neighbourhood of each pixel and calculates each local Fourier transform separately, the local Fourier transform is employed in this study to extract the MA. After that, neighbourhood analysis is performed to name the microaneurysm because the item is actually a collection of independent little images rather than the entire image. Three separate data sets and different types of performance indicators are used to examine the robustness of the proposed model. Through the prominent performance, the proposed model is able to outperform other existing models. The classification accuracy of the proposed method for MESSIDOR and ORIGA data set is 99.28% and 98.95%, respectively. [ABSTRACT FROM AUTHOR]

Globally, breast cancer (BC) is considered a major cause of death among women. Therefore, researchers have used various machine and deep learning-based methods for its early and accurate detection using X-ray, MRI, and mammography image modalities. However, the machine learning model requires domain experts to select an optimal feature, obtains a limited accuracy, and has a high false positive rate due to handcrafting features extraction. The deep learning model overcomes these limitations, but these models require large amounts of training data and computation resources, and further improvement in the model performance is needed. To do this, we employ a novel framework called the Ensemble-based Channel and Spatial Attention Network (ECS-A-Net) to automatically classify infected regions within BC images. The proposed framework consists of two phases: in the first phase, we apply different augmentation techniques to enhance the size of the input data, while the second phase includes an ensemble technique that parallelly leverages modified SE-ResNet50 and InceptionV3 as a backbone for feature extraction, followed by Channel Attention (CA) and Spatial Attention (SA) modules in a series manner for more dominant feature selection. To further validate the ECS-A-Net, we conducted extensive experiments between several competitive state-of-the-art (SOTA) techniques over two benchmarks, including DDSM and MIAS, where the proposed model achieved 96.50% accuracy for the DDSM and 95.33% accuracy for the MIAS datasets. Additionally, the experimental results demonstrated that our network achieved a better performance using various evaluation indicators, including accuracy, sensitivity, and specificity among other methods. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a growing body of research on the English learning of ethnic minority students in China, yet little is known as to how these students enact their agency to address the challenges they encounter in learning English. Through the lens of activity theory, this qualitative case study explored the manifestations and influencing factors of English learning agency of four first-year Uyghur university students. Data were collected through the students' journals, interviews, and documents, supplemented by the interview data from their teachers. The findings revealed that Uyghur students' English learning agency was manifested by reflecting on the gap btween themselves and their Han classmates and adjusting their English learning methods; adapting to the mode of all-English teaching and the rule of English presentation; using tools such as the Mandarin Chinese, English learning apps and the Internet; and seeking help from teachers, peers and other community members. The study also found that Uyghur students' agency was influenced by factors such as language proficiency, motivation, and personal learning experiences. Additionally, preferential policies of the state and the school, help from teachers and peers, and emotional support from family members also had an impact on their English learning agency. This study provides implications for the English teaching of ethnic minority students at universities. [ABSTRACT FROM AUTHOR]

Much research in recent years has focused on circular accelerators that accelerate and store secondary particles with a large momentum spread, such as muons, unstable nuclei, and heavy ions with different charge states. A fixed-field alternating gradient (FFAG) accelerator with large transverse and momentum acceptance has obvious advantages for such requirements. A versatile beam injection method is required to accelerate secondary particles with a large momentum spread and different charge states with an FFAG accelerator. In the present study, a method for charge exchange injection of positive heavy ions using the large momentum acceptance of an FFAG accelerator is proposed. A charge injection system, which converts a He1+ beam to a He2+ beam, is developed for a 150 MeV FFAG accelerator at the Center for Accelerator and Beam Applied Science (CABAS) of Kyushu University. As the first step to verify the injection method, an orbit shift from one charge state to the other is demonstrated. This is the first demonstration of heavy ion injection using an FFAG accelerator. [ABSTRACT FROM AUTHOR]

Glaucoma is a chronic disease called the silent thief of sight because it has no symptoms. When glaucoma is not identified at an early stage, it results in permanent blindness. The main objective of this research work based on the evaluation of retinal fundus images for the diagnosis of glaucoma efficiently improves the performance of glaucoma detection through higher accuracy at minimum false-positive prediction by performing preprocessing, segmentation, feature extraction, and classification. This goal is achieved through the creation of a laptop application-based computer-aided detection (CAD) system for medical professionals. The proposed CAD system would provide a second opinion as a decision made by human professionals in a controlled setting, assisting the ophthalmologist in the identification of ocular disorders. In this research work, the initial step is image acquisition; here, the input images are collected from public and in-house clinical fundus images. The next step image enhancement is performed using bilateral filter with adaptive unsharp masking (BFAUM) technique, which can eradicate the noise discern in the input images. In the subsequent step, the adaptive cluster-based superpixel segmentation (ACBSS) algorithm is implemented on an enhanced image sequence for retina blood vessel extraction and then the features are extracted using gray-level co-occurrence matrix (GLCM). Ultimately, the fundus images (public ORIGA and in-house clinical fundus images) are classified by utilizing a BMWMMBO-based DCNN classifier, the accuracy is 97.9%, and 4 FP for 699 images are predicted. The combined dataset (SCES, SINDI, and in-house clinical database) achieves an accuracy of 98.4% and 31 FP for 4501 images. [ABSTRACT FROM AUTHOR]

Cleft lip and palate (CLP) is the most common craniofacial malformation, with a range of physical, psychological, and aesthetic consequences. In this comprehensive review, our main objective is to thoroughly examine the relationship between CLP anomalies and the use of artificial intelligence (AI) in children. Additionally, we aim to explore how the integration of AI technology can bring about significant advancements in the fields of diagnosis, treatment methods, and predictive outcomes. By analyzing the existing evidence, we will highlight state-of-the-art algorithms and predictive AI models that play a crucial role in achieving precise diagnosis, susceptibility assessment, and treatment planning for children with CLP anomalies. Our focus will specifically be on the efficacy of alveolar bone graft and orthodontic interventions. The findings of this review showed that deep learning (DL) models revolutionize the diagnostic process, predict susceptibility to CLP, and enhance alveolar bone grafts and orthodontic treatment. DL models surpass human capabilities in terms of precision, and AI algorithms applied to large datasets can uncover the intricate genetic and environmental factors contributing to CLP. Additionally, Machine learning aids in preoperative planning for alveolar bone grafts and provides personalized treatment plans in orthodontic treatment. In conclusion, these advancements inspire optimism for a future where AI seamlessly integrates with CLP management, augmenting its analytical capabilities. [ABSTRACT FROM AUTHOR]

Glaucoma is one of the world leading causes of irreversible blindness. Early detection is essential to delay its progression and prevent vision loss. An accurate segmentation of the cup region in retinal fundus images is necessary to obtain relevant measurements for the detection of glaucoma. In recent years, multiple methods have been developed to automatically detect this region. All these methods are adjusted or trained using images that had been previously segmented by experts. In order to aid clinicians in performing this task, an interactive tool for the segmentation of the optic cup in stereo retinal fundus images using virtual reality has been developed. By using stereo images, the implemented virtual reality environment allows users to naturally perceive the three–dimensional structure of the optic cup region, which eases its segmentation compared to monocular images. The usage of the presented application was observed to increase accuracy of the delimitation, compared to using only two–dimensional fundus images, especially on areas with blood vessels. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) algorithms, marks a transformative progression in medical imaging diagnostics. This technical note elucidates a novel methodology for semantic segmentation of the vertebral column in CT scans, exemplified by a dataset of 250 patients from Riga East Clinical University Hospital. Our approach centers on the accurate identification and labeling of individual vertebrae, ranging from C1 to the sacrum–coccyx complex. Patient selection was meticulously conducted, ensuring demographic balance in age and sex, and excluding scans with significant vertebral abnormalities to reduce confounding variables. This strategic selection bolstered the representativeness of our sample, thereby enhancing the external validity of our findings. Our workflow streamlined the segmentation process by eliminating the need for volume stitching, aligning seamlessly with the methodology we present. By leveraging AI, we have introduced a semi-automated annotation system that enables initial data labeling even by individuals without medical expertise. This phase is complemented by thorough manual validation against established anatomical standards, significantly reducing the time traditionally required for segmentation. This dual approach not only conserves resources but also expedites project timelines. While this method significantly advances radiological data annotation, it is not devoid of challenges, such as the necessity for manual validation by anatomically skilled personnel and reliance on specialized GPU hardware. Nonetheless, our methodology represents a substantial leap forward in medical data semantic segmentation, highlighting the potential of AI-driven approaches to revolutionize clinical and research practices in radiology. [ABSTRACT FROM AUTHOR]

Ameloblastoma is a benign but locally invasive tumour. Early diagnosis of ameloblastoma and formulating an appropriate treatment plan play a crucial role in reducing the postoperative recurrence rate. The imaging features of ameloblastoma are similar to those of other odontogenic diseases; thus, the diagnosis of ameloblastoma remains challenging. This review summarises the diagnostic techniques used for the detection of ameloblastoma and the methods for differentiating it from other benign odontogenic tumours to aid in the clinical diagnosis. A comprehensive review was conducted by searching the database of PubMed for studies published up to May 2023. Panoramic radiography, computed tomography, and magnetic resonance imaging have been used to detect ameloblastoma, whereas fine-needle aspiration cytology and biopsies have been used to enhance diagnostic precision and clinical diagnosis. 18F-fluorodeoxyglucose (FDG) and FDG positron emission tomography( PET) have been used to detect metastasis of ameloblastoma to determine the prognosis of the disease. Laboratory tests are more suitable for detecting genetic alterations and identifying certain markers. Different diagnostic techniques aid in differentiating ameloblastoma from other odontogenic diseases. Combining these techniques may increase the diagnostic accuracy in patients with suspected ameloblastoma. [ABSTRACT FROM AUTHOR]

Hypertensive retinopathy (HR) results from the microvascular retinal changes triggered by hypertension, which is the most common leading cause of preventable blindness worldwide. Therefore, it is necessary to develop an automated system for HR detection and evaluation using retinal images. We aimed to propose an automated approach to identify and categorize the various degrees of HR severity. A new network called the spatial convolution module (SCM) combines cross-channel and spatial information, and the convolution operations extract helpful features. The present model is evaluated using publicly accessible datasets ODIR, INSPIREVR, and VICAVR. We applied the augmentation to artificially increase the dataset of 1200 fundus images. The different HR severity levels of normal, mild, moderate, severe, and malignant are finally classified with the reduced time when compared to the existing models because in the proposed model, convolutional layers run only once on the input fundus images, which leads to a speedup and reduces the processing time in detecting the abnormalities in the vascular structure. According to the findings, the improved SVM had the highest detection and classification accuracy rate in the vessel classification with an accuracy of 98.99% and completed the task in 160.4 s. The ten-fold classification achieved the highest accuracy of 98.99%, i.e., 0.27 higher than the five-fold classification accuracy and the improved KNN classifier achieved an accuracy of 98.72%. When computation efficiency is a priority, the proposed model's ability to quickly recognize different HR severity levels is significant. [ABSTRACT FROM AUTHOR]

Rice is a staple meal that helps people worldwide access sufficient food. However, this crop has several illnesses, significantly lowering its production and quality. Because of this, it is imperative to conduct early disease detection to halt the spread of infections. Because of this, it is desirable to develop an automatic system that will help agronomists, pathologists, and indeed growers in directly diagnosing rice diseases. This would allow for preventative measures to be done as quickly as feasible. In this day and age of artificial intelligence, researchers have experimented with various learning approaches to discover diseases that can affect rice plants. Deep learning has recently seen considerable use in many computer vision and image analysis fields, becoming one of the most prominent machine learning algorithms. Deep learning has also recently found substantial usage in many computer vision and picture analysis fields. On the other hand, deep learning methods have seen very little application in plant disease recognition, except for some ongoing research centered on the problem and using a public dataset of pictures magnified to show plant leaves. Because of their high computational complexity, which requires a huge memory cost, and the complexity of experimental materials' backgrounds, which makes it difficult to train an efficient model, deep learning methods have only seen limited use in plant disease recognition. This is due to several factors, including the following: The Inception module was improved to recognise and detect rice plant illnesses in this research by substituting the original convolutions with architecture based on modified-Xception (M-Xception). In addition, ResNet extracts features by prioritising logarithm calculations over softmax calculations to get more consistent classification outcomes. The model's training utilised a two-stage transfer learning process to produce an effective model. The results of the experiments reveal that the suggested approach can achieve the specified level of performance, with an average recognition fineness of 99.73% on the public dataset and 98.05% on the domestic dataset, respectively. Our proposed work is better as per existing methods and models. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to update the distribution of Streptococcus suis serotypes in Spain, focusing on 302 clinical isolates recovered from diseased pigs between 2020 and 2022. The most prevalent serotypes were serotype 9 (21.2%), 1 (16.2%), 2 (15.6%), 3 (6%), and 7 (5.6%). Additionally, the research focused on the study of six antimicrobial resistance genes and susceptibility to 18 antimicrobials commonly utilized in pig farming. The prevalence of the antimicrobial resistance genes was the following: tet(O) (85.8%), erm(B) (65.2%), lnu(B) (7%), lsa(E) (7%), tet(M) (6.3%), and mef(A/E) (1%). Regarding antimicrobial susceptibility testing, high resistance levels were observed, particularly for clindamycin (88.4%), chlortetracycline (89.4%), and sulfadimethoxine (94.4%). The most effective antimicrobials were ampicillin (7%), gentamicin (8.9%), and ceftiofur (9.3%). Moreover, seven significant associations were identified that correlated specific antimicrobial resistance genes with observed phenotypic resistance. This research contributes to comprehending the current S. suis serotype distribution and ongoing antimicrobial resistance developing in Spain. This study aimed to update the Streptococcus suis serotype distribution in Spain by analysing 302 clinical isolates recovered from diseased pigs between 2020 and 2022. The main objectives were to identify prevalent serotypes, differentiate specific serotypes 1, 14, 2, and 1/2, investigate specific genotypic and phenotypic antimicrobial resistance features, and explore associations between resistance genes and phenotypic resistances. Serotypes 9 (21.2%), 1 (16.2%), 2 (15.6%), 3 (6%), and 7 (5.6%) were the most prevalent, whereas serotypes 14 and 1/2 corresponded with 4.3% and 0.7% of all isolates. Antimicrobial resistance genes, including tet(O), erm(B), lnu(B), lsa(E), tet(M), and mef(A/E), were analysed, which were present in 85.8%, 65.2%, 7%, 7%, 6.3%, and 1% of the samples, respectively. Susceptibility testing for 18 antimicrobials revealed high resistance levels, particularly for clindamycin (88.4%), chlortetracycline (89.4%), and sulfadimethoxine (94.4%). Notably, seven significant associations (p < 0.0001) were detected, correlating specific antimicrobial resistance genes to the observed phenotypic resistance. These findings contribute to understanding the S. suis serotype distribution and its antibiotic resistance profiles in Spain, offering valuable insights for veterinary and public health efforts in managing S. suis-associated infections. [ABSTRACT FROM AUTHOR]

The mineralogy of niobium (Nb) is characterized by multicomponent oxides such as AB 2 O 6 , A 2 B 2 O 7 , ABO 4 , and ABO 3 in which Nb is incorporated in the B site. Such complex crystal-chemistry prevents their unambiguous identification in ore deposits such as hydrothermal rocks and laterites which exhibit complex and fine-grained textures. The understanding of the processes controlling Nb ore deposit formation in various geological settings is therefore limited, although Nb is a critical element. In this study, we use X-ray absorption spectroscopy (XAS) at the Nb K-edge to investigate the local atomic-scale structure around Nb in a large set of natural and synthetic minerals of geological and technological importance. Our X-ray absorption near-edge structure (XANES) data at the Nb K-edge show three major features of variable position and intensity and then can be related to the local distortion and coordination number of the Nb site. Shell-by-shell fits of the extended X-ray absorption fine structure (EXAFS) data reveal that the NbO 6 octahedra are distorted in a variety of pyrochlore species. At least two distinct first shells of O atoms are present while reported crystallographic data yield regular octahedra in the same minerals. Next-nearest Nb–Nb distances in pyrochlore and Nb-bearing perovskite mirror a corner-sharing NbO 6 network, whereas the two Nb–Nb distances in columbite are typical of edge- and corner-sharing NbO 6 octahedra. Such a resolution on the Nb site geometry and the intersite relationships between the next-nearest NbO 6 octahedra is made possible by collecting EXAFS data under optimal conditions at 20 K and up to 16 Å -1. The local structure around substituted Nb 5+ in Fe 3+ , Ti 4+ , and Ce 4+ oxides suffers major changes relative to the unsubstituted structures. The substitution of Nb 5+ for Ti 4+ in anatase leads to the increase in the interatomic distances between Nb and its first and second Ti 4+ neighbors. The substitution of Nb 5+ for Ce 4+ in cerianite reduces the coordination number of the cation from eight to four, and the Nb–O bonds are shortened compared to Ce–O ones. In hematite, Nb 5+ occupies a regular site, whereas the Fe 3+ site is strongly distorted suggesting major site relaxation due to charge mismatch. The sensitivity of XANES and EXAFS spectroscopies at the Nb K-edge to the local site geometry and next-nearest neighbors demonstrated in this study would help decipher Nb speciation and investigate mineralogical reactions of Nb minerals in deposit-related contexts such as hydrothermal and lateritic deposits. [ABSTRACT FROM AUTHOR]

Developments in technology have created great changes in the field of medicine and dentistry. Artificial intelligence technology is one of the most important innovations that caused this change. This study aimed to evaluate the opinions of dentistry students regarding the use of artificial intelligence in dentistry and craniomaxillofacial surgery. Two hundred ninety-six dentistry students between the ages of 19 and 30 participated in the study. Participants submitted the survey by e-mail examining the student's opinions and attitudes regarding the use of artificial intelligence in dentistry and craniomaxillofacial surgery. Respondents' anonymity was ensured. 47.30% (n: 140) of the students participating in the study are fourth-year students, and 52.70% (n: 156) are fifth-year students. While 48.98% (n: 145) of the participants have knowledge about the uses of artificial intelligence in daily life, 28.37% (n: 84) of the students have knowledge about robotic surgery. While ~74% of the participants think that artificial intelligence will improve the field of dentistry and craniomaxillofacial surgery, it has been observed that they are not worried about these applications replacing dentists in the future. It was determined that there was no statistically significant difference between fourth-year and fifth-year students in their knowledge levels about the areas of use of artificial intelligence (P=0.548). Students' opinions show that 74% agree that artificial intelligence will lead to major advances in the field of dentistry and craniomaxillofacial surgery. This shows the relationship between dentists and artificial intelligence points to a bright future., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 by Mutaz B. Habal, MD.)

Generative Adversarial Networks (GANs) have been widely applied in various domains, including medical image analysis. GANs have been utilized in classification and segmentation tasks, aiding in the detection and diagnosis of diseases and disorders. However, medical image datasets often suffer from insufficiency and imbalanced class distributions. To overcome these limitations, researchers have employed GANs to generate augmented medical images, effectively expanding datasets and balancing class distributions. This review follows the PRISMA guidelines and systematically collects peer-reviewed articles on the development of GAN-based augmentation models. Automated searches were conducted on electronic databases such as IEEE, Scopus, Science Direct, and PubMed, along with forward and backward snowballing. Out of numerous articles, 52 relevant ones published between 2018 and February 2022 were identified. The gathered information was synthesized to determine common GAN architectures, medical image modalities, body organs of interest, augmentation tasks, and evaluation metrics employed to assess model performance. Results indicated that cGAN and DCGAN were the most popular GAN architectures in the reviewed studies. Medical image modalities such as MRI, CT, X-ray, and ultrasound, along with body organs like the brain, chest, breast, and lung, were frequently used. Furthermore, the developed models were evaluated, and potential challenges and future directions for GAN-based medical image augmentation were discussed. This review presents a comprehensive overview of the current state-of-the-art in GAN-based medical image augmentation and emphasizes the potential advantages and challenges associated with GAN utilization in this domain. [ABSTRACT FROM AUTHOR]

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models, especially deep neural networks, are increasingly being used for the analysis of medical images and as a supporting tool for clinical decision-making. In this study, we propose an artificial intelligence system to facilitate dental decision-making for the removal of mandibular third molars (M3M) based on 2-dimensional orthopantograms and the risk assessment of such a procedure. A total of 4,516 panoramic radiographic images collected at the Center of Dental Medicine at the University of Zurich, Switzerland, were used for training the ML model. After image preparation and preprocessing, a spatially dependent U-Net was employed to detect and retrieve the region of the M3M and inferior alveolar nerve (IAN). Image patches identified to contain a M3M were automatically processed by a deep neural network for the classification of M3M superimposition over the IAN (task 1) and M3M root development (task 2). A control evaluation set of 120 images, collected from a different data source than the training data and labeled by 5 dental practitioners, was leveraged to reliably evaluate model performance. By 10-fold cross-validation, we achieved accuracy values of 0.94 and 0.93 for the M3M–IAN superimposition task and the M3M root development task, respectively, and accuracies of 0.9 and 0.87 when evaluated on the control data set, using a ResNet-101 trained in a semisupervised fashion. Matthew's correlation coefficient values of 0.82 and 0.75 for task 1 and task 2, evaluated on the control data set, indicate robust generalization of our model. Depending on the different label combinations of task 1 and task 2, we propose a diagnostic table that suggests whether additional imaging via 3-dimensional cone beam tomography is advisable. Ultimately, computer-aided decision-making tools benefit clinical practice by enabling efficient and risk-reduced decision-making and by supporting less experienced practitioners before the surgical removal of the M3M. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) printing, also known as additive manufacturing, is a technology used to create complex 3D structures out of a digital model that can be almost any shape. Additive manufacturing allows the creation of customized, finely detailed constructs. Improvements in 3D printing, increased 3D printer availability, decreasing costs, development of biomaterials, and improved cell culture techniques have enabled complex, novel, and customized medical applications to develop. There have been rapid development and utilization of 3D printing technologies in orthopedics, dentistry, urology, reconstructive surgery, and other health care areas. Obstetrics and Gynecology (OBGYN) is an emerging application field for 3D printing. This technology can be utilized in OBGYN for preventive medicine, early diagnosis, and timely treatment of women-and-fetus-specific health issues. Moreover, 3D printed simulations of surgical procedures enable the training of physicians according to the needs of any given procedure. Herein, we summarize the technology and materials behind additive manufacturing and review the most recent advancements in the application of 3D printing in OBGYN studies, such as diagnosis, surgical planning, training, simulation, and customized prosthesis. Furthermore, we aim to give a future perspective on the integration of 3D printing and OBGYN applications and to provide insight into the potential applications. [ABSTRACT FROM AUTHOR]

Deep learning is a powerful tool for automating the process of recognizing and classifying objects in images. In this study, we used ML.NET, a popular open-source machine learning framework, to develop a model for identifying tree species in images obtained from airborne mobile mapping. These high-resolution images can be used to create detailed maps of the landscape. They can also be analyzed and processed to extract information about visual features, including tree species recognition. The deep learning model was trained using ML.NET to classify two tree species based on the combination of airborne mobile mapping images. Our approach yielded impressive results, with a maximum classification accuracy of 93.9%. This demonstrates the effectiveness of combining imagery sources with deep learning tools in ML.NET for efficient and accurate tree species classification. This study highlights the potential of the ML.NET framework for automating object classification and can provide valuable insights and information for forestry management and conservation efforts. The primary objective of this research was to evaluate the effectiveness of an approach for identifying tree species through a model generated using a combination of ortho and oblique images captured by a mobile mapping system. [ABSTRACT FROM AUTHOR]

Persistent diabetes results in diabetic retinopathy (DR), affecting the retinal blood vessels (BVs), causing lesions. Rapid identification and treatment are crucial for preventing vision loss. Low ophthalmologist to patient's ratio results automating the DR detection a dire need. Therefore, a feature extraction method is proposed using a Mamdani fuzzy inference system (FIS) classifier for efficient identification. Mathematical morphology, region growth, and 12-region search computation have been used to mask the BVs and macula. The masked green plane image was subjected to Nick's thresholding to locate the dark lesions, from which statistical features were extracted and employed in the Mamdani FIS to classify the DR. On evaluating a total of 909 images from the MESSIDOR database shows, average sensitivity, specificity, area under the curve receiver operating characteristics, and accuracy of 99.7%, 99.8%, 99.4%, and 99.6%, respectively. The algorithm performs well in real-time images from two local hospitals. The proposed technique provides a powerful yet flexible tool for improving the diagnosis and treatment of this condition that threatens vision, as it combines the strengths of fuzzy logic, clinical knowledge, and adaptive learning to provide precise, timely, non-invasive, and economical solutions. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a cause of vision loss and blindness, and early detection of red lesions, particularly microaneurysms (MAs), is crucial to prevent its progression. In this study, we propose an effective deep learning-based DR segmentation and classification method that leverages hyper-parameter optimisation to achieve high detection accuracy. In our proposed method, we first pre-processed retinal images to reduce background variability. Then, we employed a squirrel search optimised bidirectional ConvLSTM U-net with an attention gate for DR segmentation in the main processing step. Finally, we used a Bayesian-optimised Convolutional Neural Network for classification. We selected the optimal architectures for our proposed CNNs and optimised their hyper-parameters using the Bayesian optimisation technique.Our study demonstrates that the proposed CNNs effectively classify diabetic retinopathy in fundus images when model hyper-parameters are tuned with the help of Bayesian optimisation. We conducted statistical tests based on the receiver operating characteristic curve, histogram, and ANOVA test to verify the effectiveness of our suggested models.We proposed a deep learning-based DR segmentation and classification method that leverages hyper-parameter optimisation to achieve high detection accuracy. This study highlights the potential of Bayesian optimisation as an effective technique to optimise hyper-parameters of deep learning networks for disease classification tasks. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Materials and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes. [ABSTRACT FROM AUTHOR]

Background and Objectives: The purpose of this study was to develop and evaluate a deep learning model capable of autonomously detecting and segmenting radiolucent lesions in the lower jaw by utilizing You Only Look Once (YOLO) v8. Materials and Methods: This study involved the analysis of 226 lesions present in panoramic radiographs captured between 2013 and 2023 at the Clinical Hospital Dubrava and the School of Dental Medicine, University of Zagreb. Panoramic radiographs included radiolucent lesions such as radicular cysts, ameloblastomas, odontogenic keratocysts (OKC), dentigerous cysts and residual cysts. To enhance the database, we applied techniques such as translation, scaling, rotation, horizontal flipping and mosaic effects. We have employed the deep neural network to tackle our detection and segmentation objectives. Also, to improve our model's generalization capabilities, we conducted five-fold cross-validation. The assessment of the model's performance was carried out through metrics like Intersection over Union (IoU), precision, recall and mean average precision (mAP)@50 and mAP@50-95. Results: In the detection task, the precision, recall, mAP@50 and mAP@50-95 scores without augmentation were recorded at 91.8%, 57.1%, 75.8% and 47.3%, while, with augmentation, were 95.2%, 94.4%, 97.5% and 68.7%, respectively. Similarly, in the segmentation task, the precision, recall, mAP@50 and mAP@50-95 values achieved without augmentation were 76%, 75.5%, 75.1% and 48.3%, respectively. Augmentation techniques led to an improvement of these scores to 100%, 94.5%, 96.6% and 72.2%. Conclusions: Our study confirmed that the model developed using the advanced YOLOv8 has the remarkable capability to automatically detect and segment radiolucent lesions in the mandible. With its continual evolution and integration into various medical fields, the deep learning model holds the potential to revolutionize patient care. [ABSTRACT FROM AUTHOR]

Objectives: To propose and evaluate a convolutional neural network (CNN) algorithm for automatic detection and segmentation of mucosal thickening (MT) and mucosal retention cysts (MRCs) in the maxillary sinus on low-dose and full-dose cone-beam computed tomography (CBCT)., Materials and Methods: A total of 890 maxillary sinuses on 445 CBCT scans were analyzed. The air space, MT, and MRCs in each sinus were manually segmented. Low-dose CBCTs were divided into training, training-monitoring, and testing datasets at a 7:1:2 ratio. Full-dose CBCTs were used as a testing dataset. A three-step CNN algorithm built based on V-Net and support vector regression was trained on low-dose CBCTs and tested on the low-dose and full-dose datasets. Performance for detection of MT and MRCs using area under the curves (AUCs) and for segmentation using Dice similarity coefficient (DSC) was evaluated., Results: For the detection of MT and MRCs, the algorithm achieved AUCs of 0.91 and 0.84 on low-dose scans and of 0.89 and 0.93 on full-dose scans, respectively. The median DSCs for segmenting the air space, MT, and MRCs were 0.972, 0.729, and 0.678 on low-dose scans and 0.968, 0.663, and 0.787 on full-dose scans, respectively. There were no significant differences in the algorithm performance between low-dose and full-dose CBCTs., Conclusions: The proposed CNN algorithm has the potential to accurately detect and segment MT and MRCs in maxillary sinus on CBCT scans with low-dose and full-dose protocols., Clinical Relevance: An implementation of this artificial intelligence application in daily practice as an automated diagnostic and reporting system seems possible., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: The objective of our study was to develop and validate a deep learning approach based on convolutional neural networks (CNNs) for automatic detection of the mandibular third molar (M3) and the mandibular canal (MC) and evaluation of the relationship between them on CBCT., Materials and Methods: A dataset of 254 CBCT scans with annotations by radiologists was used for the training, the validation, and the test. The proposed approach consisted of two modules: (1) detection and pixel-wise segmentation of M3 and MC based on U-Nets; (2) M3-MC relation classification based on ResNet-34. The performances were evaluated with the test set. The classification performance of our approach was compared with two residents in oral and maxillofacial radiology., Results: For segmentation performance, the M3 had a mean Dice similarity coefficient (mDSC) of 0.9730 and a mean intersection over union (mIoU) of 0.9606; the MC had a mDSC of 0.9248 and a mIoU of 0.9003. The classification models achieved a mean sensitivity of 90.2%, a mean specificity of 95.0%, and a mean accuracy of 93.3%, which was on par with the residents., Conclusions: Our approach based on CNNs demonstrated an encouraging performance for the automatic detection and evaluation of the M3 and MC on CBCT. Clinical relevance An automated approach based on CNNs for detection and evaluation of M3 and MC on CBCT has been established, which can be utilized to improve diagnostic efficiency and facilitate the precision diagnosis and treatment of M3., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: The study aimed to apply convolutional neural network (CNN) to score periapical lesion on an intraoral periapical radiograph (IOPAR) based on the periapical index (PAI) scoring system., Materials and Methods: A total of 3000 periapical root areas (PRA) on 1950 digital IOPAR were pre-scored by three endodontists. This data was used to train the CNN model-"YOLO version 3." A total of 450 PRA was used for validation of the model. Data augmentation techniques and model optimization were applied. A total of 540 PRA on 250 digital IOPAR was used to test the performance of the CNN model., Results: A total of 303 PRA (56.11%) exhibited true prediction. PAI score 1 showed the highest true prediction (90.9%). PAI scores 2 and 5 exhibited the least true prediction (30% each). PAI scores 3 and 4 had a true prediction of 60% and 71%, respectively. When the scores were dichotomized as healthy (PAI scores 1 and 2) and diseased (PAI score 3, 4, and 5), the model achieved a true prediction of 76.6% and 92%, respectively. The model exhibited a 92.1% sensitivity/recall, 76% specificity, 86.4% positive predictive value/precision, and 86.1% negative predictive value. The accuracy, F1 score, and Matthews correlation coefficient were 86.3%, 0.89, and 0.71, respectively., Conclusion: The CNN model trained on a limited amount of IOPAR data showed potential for PAI scoring of the periapical lesion on digital IOPAR., Clinical Relevance: An automated system for PAI scoring is developed that would potentially benefit clinician and researchers., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Orthopaedic surgeons need to correctly identify bone fragments using 2D/3D CT images before trauma surgery. Advances in deep learning technology provide good insights into trauma surgery over manual diagnosis. This study demonstrates the application of the DeepLab v3+ -based deep learning model for the automatic segmentation of fragments of the fractured tibia and fibula from CT images and the results of the evaluation of the performance of the automatic segmentation. The deep learning model, which was trained using over 11 million images, showed good performance with a global accuracy of 98.92%, a weighted intersection over the union of 0.9841, and a mean boundary F1 score of 0.8921. Moreover, deep learning performed 5–8 times faster than the experts' recognition performed manually, which is comparatively inefficient, with almost the same significance. This study will play an important role in preoperative surgical planning for trauma surgery with convenience and speed. [ABSTRACT FROM AUTHOR]

Background: Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence. Methods: A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set. Results: A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92). Conclusions: The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians. [ABSTRACT FROM AUTHOR]