The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer’s diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer’s disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer’s disease.
Objective To develop a deep learning system for CT images to assist in the diagnosis of thoracolumbar fractures and analyze the feasibility of its clinical application. Methods Collected from West China Hospital of Sichuan University from January 2019 to March 2020, a total of 1256 CT images of thoracolumbar fractures were annotated with a unified standard through the Imaging LabelImg system. All CT images were classified according to the AO Spine thoracolumbar spine injury classification. The deep learning system in diagnosing ABC fracture types was optimized using 1039 CT images for training and validation, of which 1004 were used as the training set and 35 as the validation set; the rest 217 CT images were used as the test set to compare the deep learning system with the clinician’s diagnosis. The deep learning system in subtyping A was optimized using 581 CT images for training and validation, of which 556 were used as the training set and 25 as the validation set; the rest 104 CT images were used as the test set to compare the deep learning system with the clinician’s diagnosis. Results The accuracy and Kappa coefficient of the deep learning system in diagnosing ABC fracture types were 89.4% and 0.849 (P<0.001), respectively. The accuracy and Kappa coefficient of subtyping A were 87.5% and 0.817 (P<0.001), respectively. Conclusions The classification accuracy of the deep learning system for thoracolumbar fractures is high. This approach can be used to assist in the intelligent diagnosis of CT images of thoracolumbar fractures and improve the current manual and complex diagnostic process.
ObjectivesTo explore the effect of the deep learning algorithm convolutional neural network (CNN) in screening of randomized controlled trials (RCTs) in Chinese medical literatures.MethodsLiterature with the topic " oral science” published in 2014 were retrieved from CNKI and exported citations containing title and abstract. RCTs screening was conducted by double independent screening, checking and peer discussion. The final results of the citations were used for CNN algorithm model training. After completing the algorithm model training, a prospective comparative trial was organized by searching all literature with the topic "oral science" published in CNKI from January to March 2018 to compare the sensitivity (SEN) and specificity (SPE) of algorithm with manual screening. The initial results of a single screener represented the performance of manual screening, and the final results after peer discussion were used as the gold standard. The best thresholds of algorithm were determined with the receptor operative characteristic (ROC) curve.ResultsA total of 1 246 RCTs and 4 754 non-RCTs were eventually included for training and testing of CNN algorithm model. 249 RCTs and 949 non-RCTs were included in the prospective trial. The SEN and SPE of manual screening were 98.01% and 98.82%. For the algorithm model, the SEN of RCTs screening decreased with the increase of threshold value while the SPE increased with the increase of threshold value. After 27 changes of threshold value, ROC curve were obtained. The area under the ROC curve was 0.9977, unveiling the optimal accuracy threshold (Threshold=0.4, SEN=98.39%, SPE=98.84%) and high sensitivity threshold (Threshold=0.06, SEN=99.60%, SPE=94.10%).ConclusionsA CNN algorithm model is trained with Chinese RCTs classification database established in this study and shows an excellent classification performance in screening RCTs of Chinese medical literature, which is proved to be comparable to the manual screening performance in the prospective controlled trial.
Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.
Cardiac three-dimensional electrophysiological labeling technology is the prerequisite and foundation of atrial fibrillation (AF) ablation surgery, and invasive labeling is the current clinical method, but there are many shortcomings such as large trauma, long procedure duration, and low success rate. In recent years, because of its non-invasive and convenient characteristics, ex vivo labeling has become a new direction for the development of electrophysiological labeling technology. With the rapid development of computer hardware and software as well as the accumulation of clinical database, the application of deep learning technology in electrocardiogram (ECG) data is becoming more extensive and has made great progress, which provides new ideas for the research of ex vivo cardiac mapping and intelligent labeling of AF substrates. This paper reviewed the research progress in the fields of ECG forward problem, ECG inverse problem, and the application of deep learning in AF labeling, discussed the problems of ex vivo intelligent labeling of AF substrates and the possible approaches to solve them, prospected the challenges and future directions for ex vivo cardiac electrophysiology labeling.
ObjectiveTo study a deep learning-based dual-modality fundus camera which was used to study retinal blood oxygen saturation and vascular morphology changes in eyes with branch retinal vein occlusion (BRVO). MethodsA prospective study. From May to October 2020, 31 patients (31 eyes) of BRVO (BRVO group) and 20 healthy volunteers (20 eyes) with matched gender and age (control group) were included in the study. Among 31 patients (31 eyes) in BRVO group, 20 patients (20 eyes) received one intravitreal injection of anti-vascular endothelial growth factor drugs before, and 11 patients (11 eyes) did not receive any treatment. They were divided into treatment group and untreated group accordingly. Retinal images were collected with a dual-modality fundus camera; arterial and vein segments were segmented in the macular region of interest (MROI) using deep learning; the optical density ratio was used to calculate retinal blood oxygen saturation (SO2) on the affected and non-involved sides of the eyes in the control group and patients in the BRVO group, and calculated the diameter, curvature, fractal dimension and density of arteriovenous in MROI. Quantitative data were compared between groups using one-way analysis of variance. ResultsThere was a statistically significant difference in arterial SO2 (SO2-A) in the MROI between the affected eyes, the fellow eyes in the BRVO group and the control group (F=4.925, P<0.001), but there was no difference in the venous SO2 (SO2-V) (F=0.607, P=0.178). Compared with the control group, the SO2-A in the MROI of the affected side and the non-involved side of the untreated group was increased, and the difference was statistically significant (F=4.925, P=0.012); there was no significant difference in SO2-V (F=0.607, P=0.550). There was no significant difference in SO2-A and SO2-V in the MROI between the affected side, the non-involved side in the treatment group and the control group (F=0.159, 1.701; P=0.854, 0.197). There was no significant difference in SO2-A and SO2-V in MROI between the affected side of the treatment group, the untreated group and the control group (F=2.553, 0.265; P=0.088, 0.546). The ophthalmic artery diameter, arterial curvature, arterial fractal dimension, vein fractal dimension, arterial density, and vein density were compared in the untreated group, the treatment group, and the control group, and the differences were statistically significant (F=3.527, 3.322, 7.251, 26.128, 4.782, 5.612; P=0.047, 0.044, 0.002, <0.001, 0.013, 0.006); there was no significant difference in vein diameter and vein curvature (F=2.132, 1.199; P=0.143, 0.321). ConclusionArterial SO2 in BRVO patients is higher than that in healthy eyes, it decreases after anti-anti-vascular endothelial growth factor drugs treatment, SO2-V is unchanged.
ObjectiveTo systematically evaluate the efficacy and safety of computer-aided detection (CADe) and conventional colonoscopy in identifying colorectal adenomas and polyps. MethodsThe PubMed, Embase, Cochrane Library, Web of Science, WanFang Data, VIP, and CNKI databases were electronically searched to collect randomized controlled trials (RCTs) comparing the effectiveness and safety of CADe assisted colonoscopy and conventional colonoscopy in detecting colorectal tumors from 2014 to April 2023. Two reviewers independently screened the literature, extracted data, and evaluated the risk of bias of the included literature. Meta-analysis was performed by RevMan 5.3 software. ResultsA total of 9 RCTs were included, with a total of 6 393 patients. Compared with conventional colonoscopy, the CADe system significantly improved the adenoma detection rate (ADR) (RR=1.22, 95%CI 1.10 to 1.35, P<0.01) and polyp detection rate (PDR) (RR=1.19, 95%CI 1.04 to 1.36, P=0.01). It also reduced the missed diagnosis rate (AMR) of adenomas (RR=0.48, 95%CI 0.34 to 0.67, P<0.01) and the missed diagnosis rate (PMR) of polyps (RR=0.39, 95%CI 0.25 to 0.59, P<0.01). The PDR of proximal polyps significantly increased, while the PDR of ≤5 mm polyps slightly increased, but the PDR of >10mm and pedunculated polyps significantly decreased. The AMR of the cecum, transverse colon, descending colon, and sigmoid colon was significantly reduced. There was no statistically significant difference in the withdrawal time between the two groups. Conclusion The CADe system can increase the detection rate of adenomas and polyps, and reduce the missed diagnosis rate. The detection rate of polyps is related to their location, size, and shape, while the missed diagnosis rate of adenomas is related to their location.
With the development of artificial intelligence (AI) technology, great progress has been made in the application of AI in the medical field. While foreign journals have published a large number of papers on the application of AI in epilepsy, there is a dearth of studies within domestic journals. In order to understand the global research progress and development trend of AI applications in epilepsy, a total of 895 papers on AI applications in epilepsy included in the Web of Science Core Collection and published before December 31, 2022 were selected as the research objects. The annual number of papers and their cited times, the most published authors, institutions and countries, and their cooperative relationships were analyzed, and the research hotspots and future trends in this field were explored by using bibliometrics and other methods. The results showed that before 2016, the annual number of papers on the application of AI in epilepsy increased slowly, and after 2017, the number of publications increased rapidly. The United States had the largest number of papers (n=273), followed by China (n=195). The institution with the largest number of papers was the University of London (n=36), and Capital Medical University in China had 23 papers. The author with the most published papers was Gregory Worrell (n=14), and the scholar with the most published articles in China was Guo Jiayan from Xiamen University (n=7). The application of machine learning in the diagnosis and treatment of epilepsy is an early research focus in this field, while the seizure prediction model based on EEG feature extraction, deep learning especially convolutional neural network application in epilepsy diagnosis, and cloud computing application in epilepsy healthcare, are the current research priorities in this field. AI-based EEG feature extraction, the application of deep learning in the diagnosis and treatment of epilepsy, and the Internet of things to solve epilepsy health-related problems are the research aims of this field in the future.
The deep learning-based automatic detection of epilepsy electroencephalogram (EEG), which can avoid the artificial influence, has attracted much attention, and its effectiveness mainly depends on the deep neural network model. In this paper, an attention-based multi-scale residual network (AMSRN) was proposed in consideration of the multiscale, spatio-temporal characteristics of epilepsy EEG and the information flow among channels, and it was combined with multiscale principal component analysis (MSPCA) to realize the automatic epilepsy detection. Firstly, MSPCA was used for noise reduction and feature enhancement of original epilepsy EEG. Then, we designed the structure and parameters of AMSRN. Among them, the attention module (AM), multiscale convolutional module (MCM), spatio-temporal feature extraction module (STFEM) and classification module (CM) were applied successively to signal reexpression with attention weighted mechanism as well as extraction, fusion and classification for multiscale and spatio-temporal features. Based on the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) public dataset, the AMSRN model achieved good results in sensitivity (98.56%), F1 score (98.35%), accuracy (98.41%) and precision (98.43%). The results show that AMSRN can make good use of brain network information flow caused by seizures to enhance the difference among channels, and effectively capture the multiscale and spatio-temporal features of EEG to improve the performance of epilepsy detection.
Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.