A web-based care assistant for caregivers of the elderly: Development and pilot study.

Background: The aging population in Korea has driven a surge in demand for elderly care services, leading to significant growth in elderly welfare facilities, particularly Adult Daycare Centers (ADCs). However, despite advancements in care facilities, caregivers continue to face challenges in providing suitable elderly care due to difficulties arising from gaps in the latest information on the elderly and their coping abilities. Objective: The objective of this study is to develop and evaluate the effectiveness of the elderly care assistant system, which facilitates the sharing of information and knowledge necessary for elderly care among caregivers. Methods: The ECA system was designed to support knowledge sharing through a knowledge management system based on an ontological knowledge model, with a web-based user interface for improved accessibility. A field trial was conducted at ADC in Seoul from August 17 to September 21, with eight caregivers participating. A mixed-methods approach, involving both surveys and interviews, was employed to gauge the ECA system's effectiveness. Results: The study found that the use of the ECA was beneficial in promoting knowledge sharing among caregivers. Additionally, caregivers noted the potential benefits of using the ECA in conjunction with family caregivers, who can offer additional information and perspectives on elderly care. Conclusions: This study presents preliminary evidence of the potential benefits of a care knowledge sharing system among various caregivers in elderly care. Although the elderly care assistant effectively promotes knowledge sharing, more research is needed to fully understand its impact on elderly care outcomes.

UAT: Universal Attention Transformer for Video Captioning.

Video captioning via encoder-decoder structures is a successful sentence generation method. In addition, using various feature extraction networks for extracting multiple features to obtain multiple kinds of visual features in the encoding process is a standard method for improving model performance. Such feature extraction networks are weight-freezing states and are based on convolution neural networks (CNNs). However, these traditional feature extraction methods have some problems. First, when the feature extraction model is used in conjunction with freezing, additional learning of the feature extraction model is not possible by exploiting the backpropagation of the loss obtained from the video captioning training. Specifically, this blocks feature extraction models from learning more about spatial information. Second, the complexity of the model is further increased when multiple CNNs are used. Additionally, the author of Vision Transformers (ViTs) pointed out the inductive bias of CNN called the local receptive field. Therefore, we propose the full transformer structure that uses an end-to-end learning method for video captioning to overcome this problem. As a feature extraction model, we use a vision transformer (ViT) and propose feature extraction gates (FEGs) to enrich the input of the captioning model through that extraction model. Additionally, we design a universal encoder attraction (UEA) that uses all encoder layer outputs and performs self-attention on the outputs. The UEA is used to address the lack of information about the video's temporal relationship because our method uses only the appearance feature. We will evaluate our model against several recent models on two benchmark datasets and show its competitive performance on MSRVTT/MSVD datasets. We show that the proposed model performed captioning using only a single feature, but in some cases, it was better than the others, which used several features.

Enhancing Targeted Minority Class Prediction in Sentence-Level Relation Extraction.

Sentence-level relation extraction (RE) has a highly imbalanced data distribution that about 80% of data are labeled as negative, i.e., no relation; and there exist minority classes (MC) among positive labels; furthermore, some of MC instances have an incorrect label. Due to those challenges, i.e., label noise and low source availability, most of the models fail to learn MC and get zero or very low F1 scores on MCs. Previous studies, however, have rather focused on micro F1 scores and MCs have not been addressed adequately. To tackle high mis-classification errors for MCs, we introduce (1) a minority class attention module (MCAM), and (2) effective augmentation methods specialized in RE. MCAM calculates the confidence scores on MC instances to select reliable ones for augmentation, and aggregates MCs information in the process of training a model. Our experiments show that our methods achieve a state-of-the-art F1 scores on TACRED as well as enhancing minority class F1 score dramatically.

Effective Pre-Training Method and Its Compositional Intelligence for Image Captioning.

With the increase in the performance of deep learning models, the model parameter has increased exponentially. An increase in model parameters leads to an increase in computation and training time, i.e., an increase in training cost. To reduce the training cost, we propose Compositional Intelligence (CI). This is a reuse method that combines pre-trained models for different tasks. Since the CI uses a well-trained model, good performance and small training cost can be expected in the target task. We applied the CI to the Image Captioning task. Compared to using a trained feature extractor, the caption generator is usually trained from scratch. On the other hand, we pre-trained the Transformer model as a caption generator and applied CI, i.e., we used a pre-trained feature extractor and a pre-trained caption generator. To compare the training cost of the From Scratch model and the CI model, early stopping was applied during fine-tuning of the image captioning task. On the MS-COCO dataset, the vanilla image captioning model reduced training cost by 13.8% and improved performance by up to 3.2%, and the Object Relation Transformer model reduced training cost by 21.3%.

Root proximity of the anchoring miniscrews of orthodontic miniplates in the mandibular incisal area: Cone-beam computed tomographic analysis.

OBJECTIVE: This outcome analysis study evaluated the actual positions of the orthodontic miniplate and miniplate anchoring screws (MPASs) and the risk factors affecting adjacent anatomic structures after miniplate placement in the mandibular incisal area. METHODS: Cone-beam computed tomographic images of 97 orthodontic miniplates and their 194 MPASs (diameter, 1.5 mm; length, 4 mm) in patients whose miniplates provided sufficient clinical stability for orthodontic treatment were retrospectively reviewed. For evaluating the actual positions of the miniplates and analyzing the risk factors, including the effects on adjacent roots, MPAS placement height (PH), placement depth (PD), plate angle (PA), mental fossa angle (MA), and root proximity were assessed using the paired t-test, analysis of variance, and generalized linear model and regression analyses. RESULTS: The mean PDs of MPASs at positions 1 (P1) and 2 (P2) were 2.01 mm and 2.23 mm, respectively. PA was significantly higher in the Class III malocclusion group than in the other groups. PH was positively correlated with MA and PD at P1. Of the 97 MPASs at P1, 49 were in the no-root area and 48 in the dentulous area; moreover, 19 showed a degree of root contact (19.6%) without root perforation. All MPASs at P2 were in the no-root area. CONCLUSIONS: Positioning the miniplate head approximately 1 mm lower than the mucogingival junction is highly likely to provide sufficient PH for the P1- MPASs to be placed in the no-root area.

HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks.

As the performance of devices that conduct large-scale computations has been rapidly improved, various deep learning models have been successfully utilized in various applications. Particularly, convolution neural networks (CNN) have shown remarkable performance in image processing tasks such as image classification and segmentation. Accordingly, more stable and robust optimization methods are required to effectively train them. However, the traditional optimizers used in deep learning still have unsatisfactory training performance for the models with many layers and weights. Accordingly, in this paper, we propose a new Adam-based hybrid optimization method called HyAdamC for training CNNs effectively. HyAdamC uses three new velocity control functions to adjust its search strength carefully in term of initial, short, and long-term velocities. Moreover, HyAdamC utilizes an adaptive coefficient computation method to prevent that a search direction determined by the first momentum is distorted by any outlier gradients. Then, these are combined into one hybrid method. In our experiments, HyAdamC showed not only notable test accuracies but also significantly stable and robust optimization abilities when training various CNN models. Furthermore, we also found that HyAdamC could be applied into not only image classification and image segmentation tasks.

A Care Knowledge Management System Based on an Ontological Model of Caring for People With Dementia: Knowledge Representation and Development Study.

BACKGROUND: Caregivers of people with dementia find it extremely difficult to choose the best care method because of complex environments and the variable symptoms of dementia. To alleviate this care burden, interventions have been proposed that use computer- or web-based applications. For example, an automatic diagnosis of the condition can improve the well-being of both the person with dementia and the caregiver. Other interventions support the individual with dementia in living independently. OBJECTIVE: The aim of this study was to develop an ontology-based care knowledge management system for people with dementia that will provide caregivers with a care guide suited to the environment and to the individual patient's symptoms. This should also enable knowledge sharing among caregivers. METHODS: To build the care knowledge model, we reviewed existing ontologies that contain concepts and knowledge descriptions relating to the care of those with dementia, and we considered dementia care manuals. The basic concepts of the care ontology were confirmed by experts in Korea. To infer the different care methods required for the individual dementia patient, the reasoning rules as defined in Semantic Web Rule Languages and Prolog were utilized. The accuracy of the care knowledge in the ontological model and the usability of the proposed system were evaluated by using the Pellet reasoner and OntOlogy Pitfall Scanner!, and a survey and interviews were conducted with caregivers working in care centers in Korea. RESULTS: The care knowledge model contains six top-level concepts: care knowledge, task, assessment, person, environment, and medical knowledge. Based on this ontological model of dementia care, caregivers at a dementia care facility in Korea were able to access the care knowledge easily through a graphical user interface. The evaluation by the care experts showed that the system contained accurate care knowledge and a level of assessment comparable to normal assessment tools. CONCLUSIONS: In this study, we developed a care knowledge system that can provide caregivers with care guides suited to individuals with dementia. We anticipate that the system could reduce the workload of caregivers.

Detection of Dental Caries and Cracks with Quantitative Light-Induced Fluorescence in Comparison to Radiographic and Visual Examination: A Retrospective Case Study.

The aim of this study was to present an optimal diagnostic protocol by comparing and analyzing a conventional examination and the quantitative light-induced fluorescence (QLF) technique. Selected were 297 teeth of 153 patients to take QLF images and bitewing radiographs. Occlusal dental caries, proximal dental caries and cracks were evaluated and scored using QLF, X-ray and/or visual criteria. The sensitivity, specificity, and area under the curve (AUC) of a receiver operating characteristic analysis were calculated. Two fluorescence parameters (|ΔFmax| and ΔRmax) were utilized to evaluate the fluorescence pattern according to the severity of lesions based on QLF or X-ray criteria. QLF showed higher scores for detecting occlusal dental caries and cracks than the conventional method. ΔRmax increased more clearly than ΔFmax did with occlusal dental caries. The |ΔFmax| values of occlusal dental caries, proximal dental caries and cracks showed good AUC levels (0.84, 0.81 and 0.83, respectively). The ΔRmax of occlusal dental caries showed the highest AUC (0.91) and the ΔRmax of proximal dental caries showed a fail level (0.59) compared to bitewing radiographs. The QLF image could visualize and estimate the degree of occlusal dental caries or cracks. Consequently, the QLF technique may be an adjunct tool to conventional methods for the detection of occlusal caries and peripheral cracks.

Effectiveness of 2D radiographs in detecting CBCT-based incidental findings in orthodontic patients.

Some craniofacial diseases or anatomical variations are found in radiographic images taken for other purposes. These incidental findings (IFs) can be detected in orthodontic patients, as various radiographs are required for orthodontic diagnosis. The radiographic data of 1020-orthodontic patients were interpreted to evaluate the rates of IFs in three-dimensional (3D) cone-beam-computed tomography (CBCT) with a large field of view (FOV) and investigate the effectiveness and accuracy of two-dimensional (2D) radiographs for detecting IFs compared to CBCT. Prevalence and accuracy in five areas was measured for sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The accuracies of various 2D-radiograph were compared through a proportion test. A total of 709-cases (69.5%) of 1020-subjects showed one or more IFs in CBCT images. Nasal cavity was the most affected area. Based on the CBCT images as a gold standard, different accuracies of various 2D-radiographs were observed in each area of the findings. The highest accuracy was confirmed in soft tissue calcifications with comprehensive radiographs. For detecting nasal septum deviations, postero-anterior cephalograms were the most accurate 2D radiograph. In cases the IFs were not determined because of its ambiguity in 2D radiographs, considering them as an absence of findings increased the accuracy.

Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model.

With recent advances in biotechnology and sequencing technology, the microbial community has been intensively studied and discovered to be associated with many chronic as well as acute diseases. Even though a tremendous number of studies describing the association between microbes and diseases have been published, text mining methods that focus on such associations have been rarely studied. We propose a framework that combines machine learning and natural language processing methods to analyze the association between microbes and diseases. A hierarchical long short-term memory network was used to detect sentences that describe the association. For the sentences determined, two different parse tree-based search methods were combined to find the relation-describing word. The ensemble model of constituency parsing for structural pattern matching and dependency-based relation extraction improved the prediction accuracy. By combining deep learning and parse tree-based extractions, our proposed framework could extract the microbe-disease association with higher accuracy. The evaluation results showed that our system achieved an F-score of 0.8764 and 0.8524 in binary decisions and extracting relation words, respectively. As a case study, we performed a large-scale analysis of the association between microbes and diseases. Additionally, a set of common microbes shared by multiple diseases were also identified in this study. This study could provide valuable information for the major microbes that were studied for a specific disease. The code and data are available at https://github.com/DMnBI/mdi_predictor .

Ultrasound-guided sialo-irrigation for the treatment of chronic sialodochitis with sialolithiasis.

Sialolithiasis is one of the most common causes of salivary duct obstruction. In the last 20 years, minimally invasive procedures like sialendoscopy, extracorporeal lithotripsy, and basket snaring are increasingly being used for the treatment of salivary gland duct stones. Sialo-irrigation of the salivary gland is an effective procedure for treating inflammation and providing symptomatic relief. This procedure can be employed for the treatment of sialolithiasis using the back pressure of instilled saline. Sialo-irrigation under ultrasound (US) guidance allows for dynamic studies showing real-time images during diagnostic or surgical procedure and can be used for the removal of sialoliths. In addition, it can also be used to remove primitive sialoliths and microliths by washing out the ductal system, which prevents the recurrence of sialoliths. The aim of this study was to propose a minimally invasive technique for sialolithiasis using US-guided sialo-irrigation.

Radiation dosimetry analyses of radiographic imaging systems used for orthodontic treatment: comparison among child, adolescent, and adult patients.

OBJECTIVES: The aim of this study was to compare the effective doses of orthodontic radiographs in children, adolescents, and adults. METHODS: We exposed a child, an adolescent (simulated by an adult female phantom), and adult male phantoms using common scanning protocols for panoramic radiography, cephalography, and cone-beam computed tomography (CBCT). Glass dosimeters were placed in the organs of the phantom to measure the absorbed doses. The effective doses were deduced using tissue weighting factors as defined in the ICRP Publication 103. RESULTS: For panoramic imaging, the parotid gland had the highest absorbed dose in the child and the submandibular glands had the highest absorbed dose in both the adolescent and adult phantoms. For cephalography, the organs and tissues located closest to the X-ray tube had the highest absorbed dose values. For CBCT, the lenses of the eyes received the highest absorbed dose. Effective doses with CBCT were the greatest in the adolescent phantom, followed by in the adult and child phantoms. CONCLUSIONS: Dental practitioners should be aware of patient age, as younger patients will incur greater risks from radiation.

Care Guide System for Caregivers of People with Dementia.

In this paper, we introduce a care guide system for caregivers of People with Dementia (PwD) at home or care facility. The system is composed of context data manager, ontological model of caring PwD, and reasoning system that adaptively generates care guides in various circumstances. Caregivers can utilize the proposed system by managing care knowledge through graphical user interface or inquire a care guide through smartphone application for text-based chatting. Knowledge models implemented in the proposed system were evaluated by the experts in caring people with dementia.

Leiomyosarcoma of the jaw: case series.

OBJECTIVES: Leiomyosarcoma is a malignant neoplasm that affects smooth muscle tissue and it is very rare in the field of oral and maxillofcial surgery. The purpose of this study was to obtain information on diagnosis of and treatment methods for leiomyosarcoma by retrospectively reviewing of the cases. PATIENTS AND METHODS: The study included nine patients who were diagnosed with leiomyosarcoma in the Department of Oral and Maxillofacial Surgery at Seoul National University Dental Hospital. The subjects were analyzed with respect to sex, age, clinical features, primary site of disease, treatment method, recurrence, and metastasis. RESULTS: Particular clinical features included pain, edema, mouth-opening limitations, dysesthesia, and enlarged lymph nodes. All cases except one were surgically treated, and recurrence was found in two cases. Four of nine patients were followed up without recurrence and one patient underwent additional surgery due to recurrence. CONCLUSION: In our case series, notable symptoms included pain, edema, mouth-opening limitations, and dysesthesia; however, it was difficult to label these as specific symptoms of leiomyosarcoma. Considering the aggressive characteristics of the disease and poor prognosis, surgical treatment is necessary with careful consideration of postoperative radiotherapy and chemotherapy.

Cone-beam computed tomography of mandibular foramen and lingula for mandibular anesthesia.

PURPOSE: The positions of the mandibular foramen (MnF) and the lingula affect the success rate of inferior alveolar nerve block. The objective of this study was to investigate aspects of the MnF and the lingula relevant for mandibular block anesthesia using cone-beam computed tomography (CBCT). MATERIALS AND METHODS: Fifty CBCT scans were collected from a picture archiving and communications system. All scans were taken using an Alphard Vega 3030 (Asahi Roentgen Co. Ltd., Kyoto, Japan). Fifty-eight MnFs of 30 subjects were included in the study. The position of the MnF, the size of the MnF, the position of the lingula, the size of the lingula, and the shape of the lingula were measured and recorded. All data were statistically analyzed at a significance level of P<0.05. RESULTS: The position of MnF was 0.1 mm and 0.8 mm below the occlusal plane in males and females, respectively. The horizontal position of the MnF was slightly anterior to the center of the ramus in males and in the center in females (P<0.05). The vertical position of the MnF was lower in females than in males (P<0.05). The MnF was an oval shape with a longer anteroposterior dimension. The height of the lingula was 9.3 mm in males and 8.2 mm in females. The nodular type was the most common shape of the lingula, followed by the triangular, truncated, and assimilated types. CONCLUSION: CBCT provided useful information about the MnF and lingula. This information could improve the success rate of mandibular blocks.

Malignant transformation of fibrous dysplasia into angiosarcoma.

Fibrous dysplasia (FD) is generally considered to be a benign disease that affects the bones, but it has potential to become malignant over time, generally several decades after its initial diagnosis. Radiation therapy can induce malignant transformation of FD; however, reports have indicated a few cases of malignant transformation of FD in the absence of radiation therapy. Angiosarcoma is a particularly rare type of cancer in the oral region, which accounts for less than 1% of all soft-tissue sarcomas. Herein, we reported a case of a 62-year-old man with monostotic FD of the left maxilla of over 50 years' duration that underwent malignant transformation into an epithelioid-type angiosarcoma. To the best of our knowledge, this is the first report of such case.

Diagnostic considerations in central odontogenic fibroma of the maxilla: 2 case reports.

Central odontogenic fibroma (COF) is defined as a fibroblastic odontogenic tumor characterized by varying density of the tooth epithelium. It is an extremely rare benign neoplasm that occurs in the maxilla and the mandible; only a few reports of COF are available in the literature. Diagnosis of the lesion based only on the radiological features of COF is difficult due to variation in the findings regarding this condition. This report describes 2 clinical cases of middle-aged women with COF. Clinical examination revealed palatal mucosal depression; additionally, oral examination, as well as panoramic radiographs, intraoral radiographs, and computed tomography scans, revealed severe root resorption. This report highlights the clinical and radiological imaging features of COF, with the goal of enabling straightforward differential diagnosis of the lesion by the clinician and thereby appropriate treatment of the patient.

Multiple implant therapy with multiple inductions of general anesthesia in non-compliant patients with schizophrenia: A case report.

The effectiveness of dental implants in patients with disability, who are non-compliant during treatment, is controversial because of their poor oral health. Thus, oral health-care and management in such patients is concerning. Moreover, limited information is available on prognosis after implant placement. Herein, we describe a patient with schizophrenia who underwent dental implantation under multiple inductions of general anesthesia (5 times) and required conservative treatment and tooth extraction for multiple dental caries and retained roots because of inadequate oral health-care. Postoperatively, fracture of the prosthodontics and progression of dental caries were observed, and with 3 additional inductions of general anesthesia, conservative treatment, implant surgery, and prosthesis implantation were conducted. Postoperative 12-month follow-up since the last prosthesis implantation showed successful results. For patients with schizophrenia, multiple implantation can reduce horizontal bone loss and achieve aesthetic results compared to treatment with removable prosthodontics and could serve as an alternative treatment modality.

Characteristic imaging findings of acantholytic squamous cell carcinoma: a case report.

Acantholytic squamous cell carcinoma (ASCC) is an uncommon, histopathologically distinct variant of squamous cell carcinoma. ASCC commonly occurs in areas of skin exposed to sunlight and has only rarely been seen on mucosal surfaces such as the oral cavity. Although the World Health Organization has defined ASCC as an original entity, the imaging findings of ASCC have not been adequately described. We herein report a case of ASCC occurring in the oral mucosa with emphasis on the findings of several imaging studies: panoramic radiography, intraoral radiography, contrast-enhanced computed tomography, magnetic resonance imaging, and fluorine-18 fluorodeoxyglucose positron emission tomography-computed tomography.

Power Doppler ultrasound-guided sialography using the phenomenon of increased blood flow: A technical report.

PURPOSE: This report presents a procedure for performing power Doppler ultrasound-guided sialography using the phenomenon of increased blood flow and illustrates its application to practical patient cases. MATERIALS AND METHODS: The salivary gland was scanned using ultrasound equipment (GE LOGIQ5 Expert® device; GE Medical Systems, Milwaukee, WI, USA) to identify pathological findings related to the patient's chief complaint. To identify the orifice of the main duct, it should be cannulated using a lacrimal dilator. After inserting the catheter into the cannulated main duct, the position of the catheter within the duct was confirmed by ultrasound. A contrast agent was injected until the patient felt fullness, and ultrasound (B-mode) was used to confirm whether the contrast agent filled the main canal and secondary and tertiary ducts. Then, power Doppler ultrasound was performed to determine whether the salivary gland had increased blood flow. RESULTS: In 2 cases in this report, a power Doppler ultrasound scan showed a significant increase in blood flow after contrast medium injection, which was not observed on a preoperative scan. CONCLUSION: Power Doppler ultrasound was found to be a simple, safe, and effective tool for real-time sialography monitoring.