Intelligent Diagnosis Research Articles

智汇岐黄，创领未来：科技赋能下的中医药传承与全球创新——2024TCMIID®中医药传承创新发展大赛综述Wisdom in Qi-Huang, Leading the Future: TCM Inheritance and Global Innovation Empowered by Technology: A Review of the 2024 TCMIID® Competition on Preservation and Innovative Development of Traditional C

2024 TCMIID® 中医药传承创新发展大赛展现了中医药与现代科技深度融合的最新成果，推动了中医药在智能诊疗、理论创新、健康管理、产业升级及国际推广等方面的发展。本综述系统分析了大赛的获奖作品，归纳总结其在人工智能赋能中医诊疗、现代中医理论创新、生态康养、绿色中医药产业化及国际传播等方面的创新点。研究发现，人工智能、大数据、区块链等技术正在推动中医药的智能化、标准化和国际化进程；中医药的理论现代化与科学验证成为行业发展的关键课题；健康管理与公共卫生结合，中医药的个性化、精准化应用日益增强。此外，绿色中医药产业链与ESG发展模式促进了行业的可持续发展。未来，中医药的高质量发展将依赖科技创新、标准化建设、国际市场拓展及绿色转型，以实现更广泛的全球影响力。 The 2024 TCMIID® Competition on the Preservation and Innovative Development of Traditional Chinese Medicine showcased the latest achievements in integrating TCM with modern technology, driving advancements in intelligent diagnosis, theoretical innovation, health management, industrial transformation, and international promotion. This review systematically analyzes the award-winning projects, summarizing their innovations in AI-powered TCM diagnostics, modernization of TCM theories, ecological wellness, green TCM industrialization, and global dissemination. The findings reveal that artificial intelligence, big data, and blockchain technologies are accelerating the intelligence, standardization, and globalization of TCM. The modernization and scientific validation of TCM theories remain critical areas for development, while personalized and precise TCM applications are increasingly incorporated into public health management. Additionally, the green TCM industry chain and ESG-driven development models are fostering sustainability in the sector. In the future, the high-quality development of TCM will rely on technological innovation, standardization, international market expansion, and green transformation, ultimately enhancing its global influence.

Artificial Intelligence in Diagnosis and Management of Nail Disorders: A Narrative Review.

Artificial intelligence (AI) is revolutionizing healthcare by enabling systems to perform tasks traditionally requiring human intelligence. In healthcare, AI encompasses various subfields, including machine learning, deep learning, natural language processing, and expert systems. In the specific domain of onychology, AI presents a promising avenue for diagnosing nail disorders, analyzing intricate patterns, and improving diagnostic accuracy. This review provides a comprehensive overview of the current applications of AI in onychology, focusing on its role in diagnosing onychomycosis, subungual melanoma, nail psoriasis, nail fold capillaroscopy, and nail involvement in systemic diseases. A literature review on AI in nail disorders was conducted via PubMed and Google Scholar, yielding relevant studies. AI algorithms, particularly deep convolutional neural networks (CNNs), have demonstrated high sensitivity and specificity in interpreting nail images, aiding differential diagnosis as well as enhancing the efficiency of diagnostic processes in a busy clinical setting. In studies evaluating onychomycosis, AI has shown the ability to distinguish between normal nails, fungal infections, and other differentials, including nail psoriasis, with a high accuracy. AI systems have proven effective in identifying subungual melanoma. For nail psoriasis, AI has been used to automate the scoring of disease severity, reducing the time and effort required. AI applications in nail fold capillaroscopy have aided the analysis of diagnosis and prognosis of connective tissue diseases. AI applications have also been extended to recognize nail manifestations of systemic diseases, by analyzing changes in nail morphology and coloration. AI also facilitates the management of nail disorders by offering tools for personalized treatment planning, remote care, treatment monitoring, and patient education. Despite these advancements, challenges such as data scarcity, image heterogeneity, interpretability issues, regulatory compliance, and poor workflow integration hinder the seamless adoption of AI in onychology practice. Ongoing research and collaboration between AI developers and nail experts is crucial to realize the full potential of AI in improving patient outcomes in onychology.

Two-dimensional nanomaterials-based optical biosensors empowered by machine learning for intelligent diagnosis

Two-dimensional nanomaterials-based optical biosensors empowered by machine learning for intelligent diagnosis

Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography

Vitreoretinal lymphoma (VRL) remains a diagnostic challenge due to its scarce prevalence, and delayed diagnosis usually results in blindness and even fatal outcomes. Herein, an artificial intelligence (AI) system is developed to identify VRL among 16 retinal diseases and conditions on optical coherence tomography (OCT) images with the cross‐subject meta‐transfer learning (CS‐MTL) algorithm. Extensive experiments of few‐shot VRL recognition tasks prove the robustness of our model on 1‐, 3‐, and 5‐shot scenarios, achieving an F1 score of 0.8697 to 0.9367. The superiority of the model is shown with a higher F1 score (0.9310) compared with other state‐of‐the‐art algorithms (0.5487–0.9018) and three doctors whose clinical experiences range between 3 to 10 years without the help of the CS‐MTL (0.7773–0.8949). AI assistance significantly improves the F1 scores of doctors by 6.16–14.46% (p < 0.001). Moreover, the F1 scores of AI‐assisted senior doctor and retinal specialist (0.9414 and 0.9500), but not the junior doctor (0.8897), exceed that of the CS‐MTL (0.9310). This study presents a promising approach for aiding in the diagnosis of VRL on retinal OCT images and may provide a novel insight into the collaboration of doctors with AI techniques, resulting in reducing the risk of diagnostic delays of rare diseases.

Intelligent diagnosis of gas pipeline condition through multivariate analysis of acoustic emission signal-based imaging

ABSTRACT Intelligent gas-pipeline condition monitoring is vital for managing risk and reducing costs, but traditional methods using acoustic emissions (AE) encounter challenges like fluid-pressure changes, flange vibrations, and inconsistent leak length, resulting in unreliable outcomes. This research proposes an intelligent solution that overcomes these obstacles, enabling real-time-accurate monitoring. The initial solution involves resampling and segmenting AE signals, followed by two-parallel processes to enhance the density of AE features. The first process involves stacking the segments to create a 2D-acoustic-time representation (ATR), while the second process uses the Hilbert transform to derive the relative magnitudes of frequency components from the envelope-power-spectrum from each segment. This results in a 2D-acoustic-frequency representation (AFR). A data-level-fusion is then proposed by combining the ATR and AFR to create the multivariate-acoustic-imaging-representation (MAIR), which serves as input for the multivariate-convolutional neural network (MCNN). Unlike previous research in this area, MAIR enables image-based input to MCNN, allowing the utilisation of image augmentation for the first time. This mitigates data limitations and enhances the generalisation performance of the model before training. Testing on the GPLA-12 dataset demonstrates the robustness of the proposed approach, achieving a pipeline-leak detection accuracy of 97.88% for 12 classes, surpassing state-of-the-art methods by at least 8.47%.

A rolling bearing fault diagnosis method under insufficient samples condition based on MSLSTM transfer learning

It usually affects the accuracy and reliability of deep learning based intelligent diagnosis methods under the condition of insufficient samples. Existing methods for handling insufficient samples often have problems such as requiring rich expert experience or consuming a lot of time. To solve the above problems, a rolling bearing fault diagnosis method under insufficient samples condition based on multi-scale long-term and short-term memory network (MSLSTM) transfer learning is proposed, which mainly consists of an improved long-term and short-term memory network named as MSLSTM and transfer learning. By introducing multi-scale convolution operation into the traditional LSTM to improve its drawback that only extracts single type of fault feature information, which leads to poor diagnostic performance in noisy environments. Besides, the pooling layer and global average pooling layer in traditional LSTM are replaced with convolution operation to avoid the problem of information loss. Subsequently, the MSLSTM is combined with transfer learning, and a rolling bearing fault diagnosis method under insufficient samples condition based on MSLSTM transfer learning is proposed, which fine tunes the model parameters using a small amount of target domain data. Feasibility of the proposed method is verified through two kinds of experiments. The proposed method has stronger feature extraction ability and training efficiency compared with other models.

Intelligent fault diagnosis based on multi-sensor data fusion and multi-scale dual attention enhanced network

Abstract As a significant component of rotating machinery, the health of rolling bearings and gears directly impact the normal operation of rotating machinery. Traditional single-sensor fault diagnosis approaches often fail to extract sufficient fault information, resulting in low diagnostic accuracy in practical engineering applications. Additionally, conventional multi-sensor fusion diagnosis methods exhibit the low robustness in noisy environments. To tackle these challenges, this paper presents a novel fault diagnosis approach derived from multi-sensor data fusion and a multi-scale dual attention enhanced network (MSDF-MSDAENet). Initially, a multi-sensor data fusion (MSDF) strategy is presented. This method reduces the dimensionality of high-dimensional data from multiple sensors to extract embedded low-dimensional effective information, which is then fused into a three-dimensional pixel matrix image. Subsequently, a multi-scale dual attention enhanced network (MSDAENet) is constructed, incorporating both time-frequency information feature extraction modules and position information extraction modules. These enhancements significantly enhance the model's capacity to extract fault features. By dividing the RGB fused images into a well-organized dataset and inputting it into the network for fault diagnosis, the method achieves intelligent diagnosis of samples with different fault types. Tests were carried out on three datasets, and the presented method was compared with various existing methods. The ultimate experimental results demonstrate the efficacy and superiority of the presented method.

Instantaneous self-recovery and ultra-low detection limit hydrogel electronic sensor for temporomandibular disorders intelligent diagnosis

Temporomandibular disorders (TMD) intelligent diagnosis promises to elevate clinical efficiency and facilitate timely TMD management for patients. However, development of TMD intelligent diagnostic tools with high accuracy and sensitivity presents challenges, particularly in sensing minute deformations and ensuring rapid self-recovery. Here we report a biocompatible hydrogel electronic sensor with instantaneous self-recovery (within 2.1 s) and ultra-low detection limit (0.005% strain). It could efficiently diagnose disc displacement with reduction (DDwR) with satisfactory accuracy of 90.00%, and also had a clear indication of the typical clinical manifestations of DDwR and the timing of temporomandibular joint (TMJ) clicking, with a sensitivity of up to 100% in human compared to the diagnostic criteria for TMD (DC/TMD). Furthermore, a predictive model based on waveform features achieved 84.4% accuracy and 86% sensitivity, reducing dependence on physicians. In summary, the hydrogel sensor is expected to become a radiation-free, non-invasive, practical and effective tool for future TMD diagnosis.

Ophthalmology : what's new in 2024

Ophthalmology is notable for significant technological innovations (e.g laser, microscopy…) that allow for diagnostic and therapeutic advances every year. This article presents the main significant therapeutic progress and highlights the growing use of artificial intelligence in diagnosis of eye disease. We include a focus on emergent virus-mediated ocular conditions that have garnered attention lately, as the number of patients with viral infections is increasing worldwide.

Interpretable Intelligent Fault Diagnosis for Heat Exchangers Based on SHAP and XGBoost

Heat exchangers play an important role in offshore oil and gas production and offshore low-temperature waste heat power generation. Heat exchanger health management mainly relies on performance parameter monitoring; however, accurate fault mode identification remains a challenge and the diagnostic results are difficult to interpret. This paper proposed an interpretable intelligent diagnosis method for heat exchangers based on Shapley Additive exPlanation and eXtreme Gradient Boosting. Firstly, secondary parameters characterizing the health state of heat exchangers are constructed based on online monitored data such as pressure and temperature. Then, the multi-dimensional feature vector is constructed by integrating the online monitored data and secondary parameters. The XGBoost fault diagnosis model for leakage and scaling faults is established with the multi-dimensional feature vector as the input. To improve the model performance on diagnosis, a grid search optimization algorithm is adopted. After the fault diagnosis of a heat exchanger is completed, the SHAP method is introduced to analyze the contribution of features by quantifying their influence on the fault diagnosis results. Finally, the effectiveness of the proposed method is verified by the heat exchanger fault simulation experiment. The proposed method achieved a diagnosis accuracy of 99.79%, which has better performance than the traditional fault diagnosis method.

Variational Mode Feature Construction-Based Improved Kernel Extreme Learning Machine for Rotating Machinery Intelligent Diagnosis

Variational Mode Feature Construction-Based Improved Kernel Extreme Learning Machine for Rotating Machinery Intelligent Diagnosis

DAQFL: Dynamic Aggregation Quantum Federated Learning Algorithm for Intelligent Diagnosis in Internet of Medical Things

DAQFL: Dynamic Aggregation Quantum Federated Learning Algorithm for Intelligent Diagnosis in Internet of Medical Things

Intra-layer and inter-layer monitoring of laser powder bed fusion defects based on airborne acoustic and gn-Res model: pore and deformation

ABSTRACT Macroscopic deformation and microscopic pores are the main defects affecting the comprehensive performance of laser powder bed fusion (LPBF) parts. The LPBF process involves highly coupled multiple physical mechanisms over a wide time scale, making it difficult to obtain strongly correlated defect characteristics and achieve online monitoring. Thus, this study proposes an acoustic-based macro–micro defect synchronous monitoring technology. It enables monitoring by analysing process information progressively from intra-layer to inter-layer. The research identifies that high energy density leads to deformation defects, with helical scan strategy energy fluctuations exacerbating deformation. A correlation between keyhole pores and high-frequency signal components allows quantitative pore analysis directly from acoustic signals. Furthermore, an intelligent diagnosis model (gn-Res_SC) is proposed, incorporating high-order information interaction and residual structure for in-depth process analysis. The model demonstrates superior performance with accuracy (90.70%), recall (90.70%) and F1-score (90.40%) in LPBF acoustic monitoring signal evaluation.

AMEF‐Net: Towards an attention and multi‐level enhancement fusion for medical image classification in Parkinson's aided diagnosis

AbstractParkinson's disease (PD) is a neurodegenerative disorder primarily affecting middle‐aged and elderly populations. Its insidious onset, high disability rate, long diagnostic cycle, and high diagnostic costs impose a heavy burden on patients and their families. Leveraging artificial intelligence, with its rapid diagnostic speed, high accuracy, and fatigue resistance, to achieve intelligent assisted diagnosis of PD holds significant promise for alleviating patients' financial stress, reducing diagnostic cycles, and helping patients seize the golden period for early treatment. This paper proposes an Attention and Multi‐level Enhancement Fusion Network (AMEF‐Net) based on the characteristics of three‐dimensional medical imaging and the specific manifestations of PD in medical images. The focus is on small lesion areas and structural lesion areas that are often overlooked in traditional deep learning models, achieving multi‐level attention and processing of imaging information. The model achieved a diagnostic accuracy of 98.867%, a precision of 99.830%, a sensitivity of 99.182%, and a specificity of 99.384% on Magnetic Resonance Images from the Parkinson's Progression Markers Initiative dataset. On Diffusion Tensor Images, it achieved a diagnostic accuracy of 99.602%, a precision of 99.930%, a sensitivity of 99.463%, and a specificity of 99.877%. The relevant code has been placed in https://github.com/EdwardTj/AMEF‐NET.

A Series Arc Fault Diagnosis Method Based on an Extreme Learning Machine Model

In this study, we address the critical issue of accurately detecting series AC arc faults, which are often challenging to identify due to their small fault currents and can lead to devastating electrical fires. We propose an intelligent diagnosis method based on the extreme learning machine (ELM) model to enhance detection accuracy and real-time monitoring capabilities. Our approach involves collecting high-frequency current signals from 23 types of loads using a self-developed AC series arc fault data acquisition device. We then extract 14 features from both the time and frequency domains as candidates for arc fault diagnosis, employing a random forest to select the most significantly changed features. Finally, we design an ELM classifier for series arc fault diagnosis, achieving an identification accuracy of 99.00% ± 0.26%. Compared to existing series arc fault diagnosis methods, our ELM-based method demonstrates superior recognition performance. This study contributes to the field by providing a more accurate and efficient diagnostic tool for series AC arc faults, with broad implications for electrical safety and fire prevention.

Edge Artificial Intelligence Device in Real-Time Endoscopy for Classification of Gastric Neoplasms: Development and Validation Study.

We previously developed artificial intelligence (AI) diagnosis algorithms for predicting the six classes of stomach lesions. However, this required significant computational resources. The incorporation of AI into medical devices has evolved from centralized models to decentralized edge computing devices. In this study, a deep learning endoscopic image classification model was created to automatically categorize all phases of gastric carcinogenesis using an edge computing device. A total of 15,910 endoscopic images were collected retrospectively and randomly assigned to train, validation, and internal-test datasets in an 8:1:1 ratio. The major outcomes were as follows: 1. lesion classification accuracy in six categories: normal/atrophy/intestinal metaplasia/dysplasia/early/advanced gastric cancer; and 2. the prospective evaluation of classification accuracy in real-world procedures. The internal-test lesion-classification accuracy was 93.8% (95% confidence interval: 93.4-94.2%); precision was 88.6%, recall was 88.3%, and F1 score was 88.4%. For the prospective performance test, the established model attained an accuracy of 93.3% (91.5-95.1%). The established model's lesion classification inference speed was 2-3 ms on GPU and 5-6 ms on CPU. The expert endoscopists reported no delays in lesion classification or any interference from the deep learning model throughout their exams. We established a deep learning endoscopic image classification model to automatically classify all stages of gastric carcinogenesis using an edge computing device.

Research on Intelligent Diagnosis of Corrosion in the Operation and Maintenance Stage of Steel Structure Engineering Based on U-Net Attention

Intelligent corrosion diagnosis plays a crucial role in enhancing the efficiency of operation and maintenance for steel structures. Presently, corrosion detection primarily depends on manual visual inspections and non-destructive testing methods, which are inefficient, costly, and subject to human bias. While machine vision has demonstrated significant potential in controlled laboratory settings, most studies have focused on environments with limited background interference, restricting their practical applicability. To tackle the challenges posed by complex backgrounds and multiple interference factors in field-collected images of steel components, this study introduces an intelligent corrosion grading method designed specifically for images containing background elements. By integrating an attention mechanism into the traditional U-Net network, we achieve precise segmentation of component pixels from background pixels in engineering images, attaining an accuracy of up to 94.1%. The proposed framework is validated using images collected from actual engineering sites. A sliding window sampling technique divides on-site images into several rectangular windows, which are filtered based on U-Net Attention segmentation results. Leveraging a dataset of steel plate corrosion images with known grades, we train an Inception v3 corrosion classification model. Transfer learning techniques are then applied to determine the corrosion grade of each filtered window, culminating in a weighted average to estimate the overall corrosion grade of the target component. This study provides a quantitative index for assessing large-scale steel structure corrosion, significantly impacting the improvement of construction and maintenance quality while laying a solid foundation for further research and development in related fields.

An intelligent fault node diagnosis and classification approach using XGBoost with whale optimization in wireless sensor networks

An intelligent fault node diagnosis and classification approach using XGBoost with whale optimization in wireless sensor networks

Exploratory Analysis of Skin Cancer Dermatoscopic Image Datasets and Classification Methods

Skin cancer is a critical global health issue, where early detection significantly improves treatment outcomes. In this review paper, titled Exploratory Analysis of Skin Cancer Dermatoscopic Image Datasets and Classification Methods, we systematically explore the intersection of artificial intelligence (AI) and skin cancer diagnosis. Our approach began with a detailed literature survey of 40 research studies, providing insights into advancements and challenges in AI-based classification methods. This survey emphasizes the role of machine learning algorithms, particularly convolutional neural networks (CNNs), and datasets such as HAM10000, ISIC 2019, and ISIC 2024 in improving diagnostic performance. Next, we conducted exploratory data analysis (EDA) on the HAM10000, ISIC 2019, and ISIC 2024 datasets, uncovering critical patterns related to lesion distribution, anatomical sites, and demographic factors. These analyses highlight biases and imbalances in the datasets, which are crucial to address for robust model training. Finally, we discuss the creation and evaluation of a machine learning model trained on a separate dataset. Initial experiments revealed challenges such as overfitting and class imbalance. Through advanced data augmentation techniques and the integration of an Augmentor pipeline, we mitigated these issues, achieving improved accuracy and generalizability. This paper provides a comprehensive framework for integrating literature insights, dataset analysis, and iterative model improvement to develop effective AI-based solutions for skin cancer detection. It underscores the importance of addressing dataset biases, adopting diverse datasets, and refining methodologies to advance AI applications in dermatology.

Large Language Models in Traditional Chinese Medicine: A Scoping Review.

The application of large language models (LLMs) in medicine has received increasing attention, showing significant potential in teaching, research, and clinical practice, especially in knowledge extraction, management, and understanding. However, the use of LLMs in Traditional Chinese Medicine (TCM) has not been thoroughly studied. This study aims to provide a comprehensive overview of the status and challenges of LLM applications in TCM. A systematic search of five electronic databases and Google Scholar was conducted between November 2022 and April 2024, using the Arksey and O'Malley five-stage framework to identify relevant studies. Data from eligible studies were comprehensively extracted and organized to describe LLM applications in TCM and assess their performance accuracy. A total of 29 studies were identified: 24 peer-reviewed articles, 1 review, and 4 preprints. Two core application areas were found: the extraction, management, and understanding of TCM knowledge, and assisted diagnosis and treatment. LLMs developed specifically for TCM achieved 70% accuracy in the TCM Practitioner Exam, while general-purpose Chinese LLMs achieved 60% accuracy. Common international LLMs did not pass the exam. Models like EpidemicCHAT and MedChatZH, trained on customized TCM corpora, outperformed general LLMs in TCM consultation. Despite their potential, LLMs in TCM face challenges such as data quality and security issues, the specificity and complexity of TCM data, and the nonquantitative nature of TCM diagnosis and treatment. Future efforts should focus on interdisciplinary talent cultivation, enhanced data standardization and protection, and exploring LLM potential in multimodal interaction and intelligent diagnosis and treatment.