METHODS: An experimental study.
OBJECTIVE: This study aimed to investigate the potential use of artificial neural networks (ANNs) in the detection of odontoid fractures using the Konstanz Information Miner (KNIME) Analytics Platform that provides a technique for computer-assisted diagnosis using radiographic X-ray imaging.
BACKGROUND: In medical image processing, computer-assisted diagnosis with ANNs from radiographic X-ray imaging is becoming increasingly popular. Odontoid fractures are a common fracture of the axis and account for 10%-15% of all cervical fractures. However, a literature review of computer-assisted diagnosis with ANNs has not been made.
METHODS: This study analyzed 432 open-mouth (odontoid) radiographic views of cervical spine X-ray images obtained from dataset repositories, which were used in developing ANN models based on the convolutional neural network theory. All the images contained diagnostic information, including 216 radiographic images of individuals with normal odontoid processes and 216 images of patients with acute odontoid fractures. The model classified each image as either showing an odontoid fracture or not. Specifically, 70% of the images were training datasets used for model training, and 30% were used for testing. KNIME\'s graphic user interface-based programming enabled class label annotation, data preprocessing, model training, and performance evaluation.
RESULTS: The graphic user interface program by KNIME was used to report all radiographic X-ray imaging features. The ANN model performed 50 epochs of training. The performance indices in detecting odontoid fractures included sensitivity, specificity, F-measure, and prediction error of 100%, 95.4%, 97.77%, and 2.3%, respectively. The model\'s accuracy accounted for 97% of the area under the receiver operating characteristic curve for the diagnosis of odontoid fractures.
CONCLUSIONS: The ANN models with the KNIME Analytics Platform were successfully used in the computer-assisted diagnosis of odontoid fractures using radiographic X-ray images. This approach can help radiologists in the screening, detection, and diagnosis of acute odontoid fractures.

OBJECTIVE: To evaluate the impact of an AI-based software trained to detect cerebral aneurysms on TOF-MRA on the diagnostic performance and reading times across readers with varying experience levels.
METHODS: One hundred eighty-six MRI studies were reviewed by six readers to detect cerebral aneurysms. Initially, readings were assisted by the CNN-based software mdbrain. After 6 weeks, a second reading was conducted without software assistance. The results were compared to the consensus reading of two neuroradiological specialists and sensitivity (lesion and patient level), specificity (patient level), and false positives per case were calculated for the group of all readers, for the subgroup of physicians, and for each individual reader. Also, reading times for each reader were measured.
RESULTS: The dataset contained 54 aneurysms. The readers had no experience (three medical students), 2 years experience (resident in neuroradiology), 6 years experience (radiologist), and 12 years (neuroradiologist). Significant improvements of overall specificity and the overall number of false positives per case were observed in the reading with AI support. For the physicians, we found significant improvements of sensitivity on lesion and patient level and false positives per case. Four readers experienced reduced reading times with the software, while two encountered increased times.
CONCLUSIONS: In the reading with the AI-based software, we observed significant improvements in terms of specificity and false positives per case for the group of all readers and significant improvements of sensitivity and false positives per case for the physicians. Further studies are needed to investigate the effects of the AI-based software in a prospective setting.

UNASSIGNED: Recently, an artificial intelligence-based computer-assisted diagnosis (AI-CAD) for panoramic radiography was developed to scan the inferior margin of the mandible and automatically evaluate mandibular cortical morphology. The aim of this study was to analyze quantitatively the mandibular cortical morphology using the AI-CAD, especially focusing on underlying diseases and dental status in women over 20 years of age.
UNASSIGNED: 419 patients in women over 20 years of age who underwent panoramic radiography were included in this study. The mandibular cortical morphology was analyzed with an AI-CAD that evaluated the degree of deformation of the mandibular inferior cortex (MIC) and mandibular cortical index (MCI) automatically. Those were analyzed in relation to underlying diseases, such as diabetes, hypertension, dyslipidemia, rheumatism and osteoporosis, and dental status, such as the number of teeth present in the maxilla and mandible.
UNASSIGNED: The degree of deformation of MIC in women under 51 years of age (21-50 years; n = 229, 16.0 ± 12.7) was significantly lower than those of over 50 years of age (51-90 years; n = 190, 45.1 ± 23.0), and the MCI was a significant difference for the different age group. Regarding the degree of deformation of MIC and MCI in women over 50 years of age, osteoporosis and number of total teeth present in the maxilla and mandible were significant differences.
UNASSIGNED: The results of this study indicated that the mandibular cortical morphology using the AI-CAD is significantly related to osteoporosis and dental status in women over 50 years of age.

UNASSIGNED: The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer\'s disease (AD), as the most common type of dementia, has become more frequent too.
UNASSIGNED: Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future.
UNASSIGNED: Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected.
UNASSIGNED: The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field.
UNASSIGNED: The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

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Purpose: To construct a deep learning knowledge distillation framework exploring the utilization of MRI alone or combing with distilled Arthroscopy information for meniscus tear detection. Methods: A database of 199 paired knee Arthroscopy-MRI exams was used to develop a multimodal teacher network and an MRI-based student network, which used residual neural networks architectures. A knowledge distillation framework comprising the multimodal teacher network T and the monomodal student network S was proposed. We optimized the loss functions of mean squared error (MSE) and cross-entropy (CE) to enable the student network S to learn arthroscopic information from the teacher network T through our deep learning knowledge distillation framework, ultimately resulting in a distilled student network S T. A coronal proton density (PD)-weighted fat-suppressed MRI sequence was used in this study. Fivefold cross-validation was employed, and the accuracy, sensitivity, specificity, F1-score, receiver operating characteristic (ROC) curves and area under the receiver operating characteristic curve (AUC) were used to evaluate the medial and lateral meniscal tears detection performance of the models, including the undistilled student model S, the distilled student model S T and the teacher model T. Results: The AUCs of the undistilled student model S, the distilled student model S T, the teacher model T for medial meniscus (MM) tear detection and lateral meniscus (LM) tear detection are 0.773/0.672, 0.792/0.751 and 0.834/0.746, respectively. The distilled student model S T had higher AUCs than the undistilled model S. After undergoing knowledge distillation processing, the distilled student model demonstrated promising results, with accuracy (0.764/0.734), sensitivity (0.838/0.661), and F1-score (0.680/0.754) for both medial and lateral tear detection better than the undistilled one with accuracy (0.734/0.648), sensitivity (0.733/0.607), and F1-score (0.620/0.673). Conclusion: Through the knowledge distillation framework, the student model S based on MRI benefited from the multimodal teacher model T and achieved an improved meniscus tear detection performance.

Ultrasonography is the preferred modality for detailed evaluation of enlarged lymph nodes (LNs) identified on computed tomography and/or magnetic resonance imaging, owing to its high spatial resolution. However, the diagnostic performance of ultrasonography depends on the examiner\'s expertise. To support the ultrasonographic diagnosis, we developed YOLOv7-based deep learning models for metastatic LN detection on ultrasonography and compared their detection performance with that of highly experienced radiologists and less experienced residents. We enrolled 462 B- and D-mode ultrasound images of 261 metastatic and 279 non-metastatic histopathologically confirmed LNs from 126 patients with head and neck squamous cell carcinoma. The YOLOv7-based B- and D-mode models were optimized using B- and D-mode training and validation images and their detection performance for metastatic LNs was evaluated using B- and D-mode testing images, respectively. The D-mode model\'s performance was comparable to that of radiologists and superior to that of residents\' reading of D-mode images, whereas the B-mode model\'s performance was higher than that of residents but lower than that of radiologists on B-mode images. Thus, YOLOv7-based B- and D-mode models can assist less experienced residents in ultrasonographic diagnoses. The D-mode model could raise the diagnostic performance of residents to the same level as experienced radiologists.

OBJECTIVE: This study aimed to investigate whether the accuracy of intraoral scanners is influenced by different scanning strategies in an in vitro setting, through a systematic review and meta-analysis.
METHODS: This review was conducted in accordance with the PRISMA 2020 standard. The following PICOS approach was used: population, tooth impressions; intervention, the use of intraoral scanners with scanning strategies different from the manufacturer\'s instructions; control, the use of intraoral scanners following the manufacturers\' requirements; outcome, accuracy of intraoral scanners; type of studies, in vitro. A comprehensive literature search was conducted across various databases including Embase, SciELO, PubMed, Scopus, and Web of Science. The inclusion criteria were based on in vitro studies that reported the accuracy of digital impressions using intraoral scanners. Analysis was performed using Review Manager software (version 5.3.5; Cochrane Collaboration, Copenhagen, Denmark). Global comparisons were made using a standardized mean difference based on random-effect models, with a significance level of α = 0.05.
RESULTS: The meta-analysis included 15 articles. Digital impression accuracy significantly improved under dry conditions (P < 0.001). Moreover, trueness and precision were enhanced when artificial landmarks were used (P ≤ 0.02) and when an S-shaped pattern was followed (P ≤ 0.01). However, the type of light used did not have a significant impact on the accuracy of the digital intraoral scanners (P ≥ 0.16).
CONCLUSIONS: The accuracy of digital intraoral scanners can be enhanced by employing scanning processes using artificial landmarks and digital impressions under dry conditions.

OBJECTIVE: This study aimed to evaluate the utility of large language model (LLM) artificial intelligence tools, Chat Generative Pre-Trained Transformer (ChatGPT) versions 3.5 and 4, in managing complex otolaryngological clinical scenarios, specifically for the multidisciplinary management of odontogenic sinusitis (ODS).
METHODS: A prospective, structured multidisciplinary specialist evaluation was conducted using five ad hoc designed ODS-related clinical scenarios. LLM responses to these scenarios were critically reviewed by a multidisciplinary panel of eight specialist evaluators (2 ODS experts, 2 rhinologists, 2 general otolaryngologists, and 2 maxillofacial surgeons). Based on the level of disagreement from panel members, a Total Disagreement Score (TDS) was calculated for each LLM response, and TDS comparisons were made between ChatGPT3.5 and ChatGPT4, as well as between different evaluators.
RESULTS: While disagreement to some degree was demonstrated in 73/80 evaluator reviews of LLMs\' responses, TDSs were significantly lower for ChatGPT4 compared to ChatGPT3.5. Highest TDSs were found in the case of complicated ODS with orbital abscess, presumably due to increased case complexity with dental, rhinologic, and orbital factors affecting diagnostic and therapeutic options. There were no statistically significant differences in TDSs between evaluators\' specialties, though ODS experts and maxillofacial surgeons tended to assign higher TDSs.
CONCLUSIONS: LLMs like ChatGPT, especially newer versions, showed potential for complimenting evidence-based clinical decision-making, but substantial disagreement was still demonstrated between LLMs and clinical specialists across most case examples, suggesting they are not yet optimal in aiding clinical management decisions. Future studies will be important to analyze LLMs\' performance as they evolve over time.

Computer-assisted polyp characterization (computer-aided diagnosis, CADx) facilitates optical diagnosis during colonoscopy. Several studies have demonstrated high sensitivity and specificity of CADx tools in identifying neoplastic changes in colorectal polyps. To implement CADx tools in colonoscopy, there is a need to confirm whether these tools satisfy the threshold levels that are required to introduce optical diagnosis strategies such as \"diagnose-and-leave,\" \"resect-and-discard\" or \"DISCARD-lite.\" In this article, we review the available data from prospective trials regarding the effect of multiple CADx tools and discuss whether they meet these thresholds.