Developing and Validating an Intelligent Mouth-Opening Training Device: A New Solution for Restricted Mouth Opening.

Restricted mouth opening (trismus) is one of the most common complications following head and neck cancer treatment. Early initiation of mouth-opening exercises is crucial for preventing or minimizing trismus. Current methods for these exercises predominantly involve finger exercises and traditional mouth-opening training devices. Our research group successfully designed an intelligent mouth-opening training device (IMOTD) that addresses the limitations of traditional home training methods, including the inability to quantify mouth-opening exercises, a lack of guided training resulting in temporomandibular joint injuries, and poor training continuity leading to poor training effect. For this device, an interactive remote guidance mode is introduced to address these concerns. The device was designed with a focus on the safety and effectiveness of medical devices. The accuracy of the training data was verified through piezoelectric sensor calibration. Through mechanical analysis, the stress points of the structure were identified, and finite element analysis of the connecting rod and the occlusal plate connection structure was conducted to ensure the safety of the device. The findings support the effectiveness of the intelligent device in rehabilitation through preclinical experiments when compared with conventional mouth-opening training methods. This intelligent device facilitates the quantification and visualization of mouth-opening training indicators, ensuring both the comfort and safety of the training process. Additionally, it enables remote supervision and guidance for patient training, thereby enhancing patient compliance and ultimately ensuring the effectiveness of mouth-opening exercises.

Task-Specific Normalization for Continual Learning of Blind Image Quality Models.

In this paper, we present a simple yet effective continual learning method for blind image quality assessment (BIQA) with improved quality prediction accuracy, plasticity-stability trade-off, and task-order/-length robustness. The key step in our approach is to freeze all convolution filters of a pre-trained deep neural network (DNN) for an explicit promise of stability, and learn task-specific normalization parameters for plasticity. We assign each new IQA dataset (i.e., task) a prediction head, and load the corresponding normalization parameters to produce a quality score. The final quality estimate is computed by a weighted summation of predictions from all heads with a lightweight K -means gating mechanism. Extensive experiments on six IQA datasets demonstrate the advantages of the proposed method in comparison to previous training techniques for BIQA.

A novel approach to the detection of facial wrinkles: Database, detection algorithm, and evaluation metrics.

Skin wrinkles result from intrinsic aging processes and extrinsic influences, including prolonged exposure to ultraviolet radiation and tobacco smoking. Hence, the identification of wrinkles holds significant importance in skin aging and medical aesthetic investigation. Nevertheless, current methods lack the comprehensiveness to identify facial wrinkles, particularly those that may appear insignificant. Furthermore, the current assessment techniques neglect to consider the blurred boundary of wrinkles and cannot differentiate images with varying resolutions. This research introduces a novel wrinkle detection algorithm and a distance-based loss function to identify full-face wrinkles. Furthermore, we develop a wrinkle detection evaluation metric that assesses outcomes based on curve, location, and gradient similarity. We collected and annotated a dataset for wrinkle detection consisting of 1021 images of Chinese faces. The dataset will be made publicly available to further promote wrinkle detection research. The research demonstrates a substantial enhancement in detecting subtle wrinkles through implementing the proposed method. Furthermore, the suggested evaluation procedure effectively considers the indistinct boundaries of wrinkles and is applicable to images with various resolutions.

Assessing the Capabilities of Generative Pretrained Transformer-4 in Addressing Open-Ended Inquiries of Oral Cancer.

INTRODUCTION AND AIMS: In the face of escalating oral cancer rates, the application of large language models like Generative Pretrained Transformer (GPT)-4 presents a novel pathway for enhancing public awareness about prevention and early detection. This research aims to explore the capabilities and possibilities of GPT-4 in addressing open-ended inquiries in the field of oral cancer. METHODS: Using 60 questions accompanied by reference answers, covering concepts, causes, treatments, nutrition, and other aspects of oral cancer, evaluators from diverse backgrounds were selected to evaluate the capabilities of GPT-4 and a customized version. A P value under .05 was considered significant. RESULTS: Analysis revealed that GPT-4 and its adaptations notably excelled in answering open-ended questions, with the majority of responses receiving high scores. Although the median score for standard GPT-4 was marginally better, statistical tests showed no significant difference in capabilities between the two models (P > .05). Despite statistical significance indicated diverse backgrounds of evaluators have statistically difference (P < .05), a post hoc test and comprehensive analysis demonstrated that both editions of GPT-4 demonstrated equivalent capabilities in answering questions concerning oral cancer. CONCLUSIONS: GPT-4 has demonstrated its capability to furnish responses to open-ended inquiries concerning oral cancer. Utilizing this advanced technology to boost public awareness about oral cancer is viable and has much potential. When it's unable to locate pertinent information, it will resort to their inherent knowledge base or recommend consulting professionals after offering some basic information. Therefore, it cannot supplant the expertise and clinical judgment of surgical oncologists and could be used as an adjunctive evaluation tool.

Neighborhood evaluator for efficient super-resolution reconstruction of 2D medical images.

BACKGROUND: Deep learning-based super-resolution (SR) algorithms aim to reconstruct low-resolution (LR) images into high-fidelity high-resolution (HR) images by learning the low- and high-frequency information. Experts' diagnostic requirements are fulfilled in medical application scenarios through the high-quality reconstruction of LR digital medical images. PURPOSE: Medical image SR algorithms should satisfy the requirements of arbitrary resolution and high efficiency in applications. However, there is currently no relevant study available. Several SR research on natural images have accomplished the reconstruction of resolutions without limitations. However, these methodologies provide challenges in meeting medical applications due to the large scale of the model, which significantly limits efficiency. Hence, we suggest a highly effective method for reconstructing medical images at any desired resolution. METHODS: Statistical features of medical images exhibit greater continuity in the region of neighboring pixels than natural images. Hence, the process of reconstructing medical images is comparatively less challenging. Utilizing this property, we develop a neighborhood evaluator to represent the continuity of the neighborhood while controlling the network's depth. RESULTS: The suggested method has superior performance across seven scales of reconstruction, as evidenced by experiments conducted on panoramic radiographs and two external public datasets. Furthermore, the proposed network significantly decreases the parameter count by over 20× and the computational workload by over 10× compared to prior researches. On large-scale reconstruction, the inference speed can be enhanced by over 5×. CONCLUSION: The novel proposed SR strategy for medical images performs efficient reconstruction at arbitrary resolution, marking a significant breakthrough in the field. The given scheme facilitates the implementation of SR in mobile medical platforms.

Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models.

Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users' viewing experience in various real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. Thus, it is crucial to gain a better understanding of existing VQA datasets in order to properly evaluate the current progress in BVQA. Towards this goal, we conduct a first-of-its-kind computational analysis of VQA datasets via designing minimalistic BVQA models. By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations. By comparing the quality prediction performance of different model variants on eight VQA datasets with realistic distortions, we find that nearly all datasets suffer from the easy dataset problem of varying severity, some of which even admit blind image quality assessment (BIQA) solutions. We additionally justify our claims by comparing our model generalization capabilities on these VQA datasets, and by ablating a dizzying set of BVQA design choices related to the basic building blocks. Our results cast doubt on the current progress in BVQA, and meanwhile shed light on good practices of constructing next-generation VQA datasets and models.

A Coding Framework and Benchmark Towards Low-Bitrate Video Understanding.

Video compression is indispensable to most video analysis systems. Despite saving the transportation bandwidth, it also deteriorates downstream video understanding tasks, especially at low-bitrate settings. To systematically investigate this problem, we first thoroughly review the previous methods, revealing that three principles, i.e., task-decoupled, label-free, and data-emerged semantic prior, are critical to a machine-friendly coding framework but are not fully satisfied so far. In this paper, we propose a traditional-neural mixed coding framework that simultaneously fulfills all these principles, by taking advantage of both traditional codecs and neural networks (NNs). On one hand, the traditional codecs can efficiently encode the pixel signal of videos but may distort the semantic information. On the other hand, highly non-linear NNs are proficient in condensing video semantics into a compact representation. The framework is optimized by ensuring that a transportation-efficient semantic representation of the video is preserved w.r.t. the coding procedure, which is spontaneously learned from unlabeled data in a self-supervised manner. The videos collaboratively decoded from two streams (codec and NN) are of rich semantics, as well as visually photo-realistic, empirically boosting several mainstream downstream video analysis task performances without any post-adaptation procedure. Furthermore, by introducing the attention mechanism and adaptive modeling scheme, the video semantic modeling ability of our approach is further enhanced. Fianlly, we build a low-bitrate video understanding benchmark with three downstream tasks on eight datasets, demonstrating the notable superiority of our approach. All codes, data, and models will be open-sourced for facilitating future research.

Xiaoqing: A Q&A model for glaucoma based on LLMs.

Glaucoma is one of the leading cause of blindness worldwide. Individuals affected by glaucoma, including patients and their family members, frequently encounter a deficit in dependable support beyond the confines of clinical environments. Seeking advice via the internet can be a difficult task due to the vast amount of disorganized and unstructured material available on these sites, nevertheless. This research explores how Large Language Models (LLMs) can be leveraged to better serve medical research and benefit glaucoma patients. We introduce Xiaoqing, a Natural Language Processing (NLP) model specifically tailored for the glaucoma field, detailing its development and deployment. To evaluate its effectiveness, we conducted two forms of experiments: comparative and experiential. In the comparative analysis, we presented 22 glaucoma-related questions in simplified Chinese to three medical NLP models (Xiaoqing LLMs, HuaTuo, Ivy GPT) and two general models (ChatGPT-3.5 and ChatGPT-4), covering a range of topics from basic glaucoma knowledge to treatment, surgery, research, management standards, and patient lifestyle. Responses were assessed for informativeness and readability. The experiential experiment involved glaucoma patients and non-patients interacting with Xiaoqing, collecting and analyzing their questions and feedback on the same criteria. The findings demonstrated that Xiaoqing notably outperformed the other models in terms of informativeness and readability, suggesting that Xiaoqing is a significant advancement in the management and treatment of glaucoma in China. We also provide a Web-based version of Xiaoqing, allowing readers to directly experience its functionality. The Web-based Xiaoqing is available at https://qa.glaucoma-assistant.com//qa.

Prediction model of radiotherapy outcome for Ocular Adnexal Lymphoma using informative features selected by chemometric algorithms.

BACKGROUND: Ocular Adnexal Lymphoma (OAL) is a non-Hodgkin's lymphoma that most often appears in the tissues near the eye, and radiotherapy is the currently preferred treatment. There has been a controversy regarding the prognostic factors for systemic failure of OAL radiotherapy, the thorough evaluation prior to receiving radiotherapy is highly recommended to better the patient's prognosis and minimize the likelihood of any adverse effects. PURPOSE: To investigate the risk factors that contribute to incomplete remission in OAL radiotherapy and to establish a hybrid model for predicting the radiotherapy outcomes in OAL patients. METHODS: A retrospective chart review was performed for 87 consecutive patients with OAL who received radiotherapy between Feb 2011 and August 2022 in our center. Seven image features, derived from MRI sequences, were integrated with 122 clinical features to form comprehensive patient feature sets. Chemometric algorithms were then employed to distill highly informative features from these sets. Based on these refined features, SVM and XGBoost classifiers were performed to classify the effect of radiotherapy. RESULTS: The clinical records of from 87 OAL patients (median age: 60 months, IQR: 52-68 months; 62.1% male) treated with radiotherapy were reviewed. Analysis of Lasso (AUC = 0.75, 95% CI: 0.72-0.77) and Random Forest (AUC = 0.67, 95% CI: 0.62-0.70) algorithms revealed four potential features, resulting in an intersection AUC of 0.80 (95% CI: 0.75-0.82). Logistic Regression (AUC = 0.75, 95% CI: 0.72-0.77) identified two features. Furthermore, the integration of chemometric methods such as CARS (AUC = 0.66, 95% CI: 0.62-0.72), UVE (AUC = 0.71, 95% CI: 0.66-0.75), and GA (AUC = 0.65, 95% CI: 0.60-0.69) highlighted six features in total, with an intersection AUC of 0.82 (95% CI: 0.78-0.83). These features included enophthalmos, diplopia, tenderness, elevated ALT count, HBsAg positivity, and CD43 positivity in immunohistochemical tests. CONCLUSION: The findings suggest the effectiveness of chemometric algorithms in pinpointing OAL risk factors, and the prediction model we proposed shows promise in helping clinicians identify OAL patients likely to achieve complete remission via radiotherapy. Notably, patients with a history of exophthalmos, diplopia, tenderness, elevated ALT levels, HBsAg positivity, and CD43 positivity are less likely to attain complete remission after radiotherapy. These insights offer more targeted management strategies for OAL patients. The developed model is accessible online at: https://lzz.testop.top/.

A cross-temporal multimodal fusion system based on deep learning for orthodontic monitoring.

INTRODUCTION: In the treatment of malocclusion, continuous monitoring of the three-dimensional relationship between dental roots and the surrounding alveolar bone is essential for preventing complications from orthodontic procedures. Cone-beam computed tomography (CBCT) provides detailed root and bone data, but its high radiation dose limits its frequent use, consequently necessitating an alternative for ongoing monitoring. OBJECTIVES: We aimed to develop a deep learning-based cross-temporal multimodal image fusion system for acquiring root and jawbone information without additional radiation, enhancing the ability of orthodontists to monitor risk. METHODS: Utilizing CBCT and intraoral scans (IOSs) as cross-temporal modalities, we integrated deep learning with multimodal fusion technologies to develop a system that includes a CBCT segmentation model for teeth and jawbones. This model incorporates a dynamic kernel prior model, resolution restoration, and an IOS segmentation network optimized for dense point clouds. Additionally, a coarse-to-fine registration module was developed. This system facilitates the integration of IOS and CBCT images across varying spatial and temporal dimensions, enabling the comprehensive reconstruction of root and jawbone information throughout the orthodontic treatment process. RESULTS: The experimental results demonstrate that our system not only maintains the original high resolution but also delivers outstanding segmentation performance on external testing datasets for CBCT and IOSs. CBCT achieved Dice coefficients of 94.1 % and 94.4 % for teeth and jawbones, respectively, and it achieved a Dice coefficient of 91.7 % for the IOSs. Additionally, in the context of real-world registration processes, the system achieved an average distance error (ADE) of 0.43 mm for teeth and 0.52 mm for jawbones, significantly reducing the processing time. CONCLUSION: We developed the first deep learning-based cross-temporal multimodal fusion system, addressing the critical challenge of continuous risk monitoring in orthodontic treatments without additional radiation exposure. We hope that this study will catalyze transformative advancements in risk management strategies and treatment modalities, fundamentally reshaping the landscape of future orthodontic practice.

CT-based artificial intelligence prediction model for ocular motility score of thyroid eye disease.

PURPOSE: Thyroid eye disease (TED) is the most common orbital disease in adults. Ocular motility restriction is the primary complaint of patients, while its evaluation is quite difficult. The present study aimed to introduce an artificial intelligence (AI) model based on orbital computed tomography (CT) images for ocular motility score. METHODS: A total of 410 sets of CT images and clinical data were obtained from the hospital. To build a triple classification predictive model for ocular motility score, multiple deep learning models were employed to extract features of images and clinical data. Subgroup analyses based on pertinent clinical features were performed to test the efficacy of models. RESULTS: The ResNet-34 network outperformed Alex-Net and VGG16-Net in prediction of ocular motility score, with the optimal accuracy (ACC) of 0.907, 0.870, and 0.890, respectively. Subgroup analyses indicated no significant difference in ACC between active or inactive phase, functional visual field diplopia or peripheral visual field diplopia (p > 0.05). However, in the gender subgroup, the prediction model performed more accurately in female patients than males (p = 0.02). CONCLUSION: In conclusion, the AI model based on CT images and clinical data successfully realized automatic scoring of ocular motility in TED patients. This approach potentially enhanced the efficiency and accuracy of ocular motility evaluation, thus facilitating clinical application.

Artificial Intelligence for Early Detection of Pediatric Eye Diseases Using Mobile Photos.

IMPORTANCE: Identifying pediatric eye diseases at an early stage is a worldwide issue. Traditional screening procedures depend on hospitals and ophthalmologists, which are expensive and time-consuming. Using artificial intelligence (AI) to assess children's eye conditions from mobile photographs could facilitate convenient and early identification of eye disorders in a home setting. OBJECTIVE: To develop an AI model to identify myopia, strabismus, and ptosis using mobile photographs. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study was conducted at the Department of Ophthalmology of Shanghai Ninth People's Hospital from October 1, 2022, to September 30, 2023, and included children who were diagnosed with myopia, strabismus, or ptosis. MAIN OUTCOMES AND MEASURES: A deep learning-based model was developed to identify myopia, strabismus, and ptosis. The performance of the model was assessed using sensitivity, specificity, accuracy, the area under the curve (AUC), positive predictive values (PPV), negative predictive values (NPV), positive likelihood ratios (P-LR), negative likelihood ratios (N-LR), and the F1-score. GradCAM++ was utilized to visually and analytically assess the impact of each region on the model. A sex subgroup analysis and an age subgroup analysis were performed to validate the model's generalizability. RESULTS: A total of 1419 images obtained from 476 patients (225 female [47.27%]; 299 [62.82%] aged between 6 and 12 years) were used to build the model. Among them, 946 monocular images were used to identify myopia and ptosis, and 473 binocular images were used to identify strabismus. The model demonstrated good sensitivity in detecting myopia (0.84 [95% CI, 0.82-0.87]), strabismus (0.73 [95% CI, 0.70-0.77]), and ptosis (0.85 [95% CI, 0.82-0.87]). The model showed comparable performance in identifying eye disorders in both female and male children during sex subgroup analysis. There were differences in identifying eye disorders among different age subgroups. CONCLUSIONS AND RELEVANCE: In this cross-sectional study, the AI model demonstrated strong performance in accurately identifying myopia, strabismus, and ptosis using only smartphone images. These results suggest that such a model could facilitate the early detection of pediatric eye diseases in a convenient manner at home.