ObjectiveTo apply the multi-modal deep learning model to automatically classify the ultra-widefield fluorescein angiography (UWFA) images of diabetic retinopathy (DR). MethodsA retrospective study. From 2015 to 2020, 798 images of 297 DR patients with 399 eyes who were admitted to Eye Center of Renmin Hospital of Wuhan University and were examined by UWFA were used as the training set and test set of the model. Among them, 119, 171, and 109 eyes had no retinopathy, non-proliferative DR (NPDR), and proliferative DR (PDR), respectively. Localization and assessment of fluorescein leakage and non-perfusion regions in early and late orthotopic images of UWFA in DR-affected eyes by jointly optimizing CycleGAN and a convolutional neural network (CNN) classifier, an image-level supervised deep learning model. The abnormal images with lesions were converted into normal images with lesions removed using the improved CycleGAN, and the difference images containing the lesion areas were obtained; the difference images were classified by the CNN classifier to obtain the prediction results. A five-fold cross-test was used to evaluate the classification accuracy of the model. Quantitative analysis of the marker area displayed by the differential images was performed to observe the correlation between the ischemia index and leakage index and the severity of DR. ResultsThe generated fake normal image basically removed all the lesion areas while retaining the normal vascular structure; the difference images intuitively revealed the distribution of biomarkers; the heat icon showed the leakage area, and the location was basically the same as the lesion area in the original image. The results of the five-fold cross-check showed that the average classification accuracy of the model was 0.983. Further quantitative analysis of the marker area showed that the ischemia index and leakage index were significantly positively correlated with the severity of DR (β=6.088, 10.850; P＜0.001). ConclusionThe constructed multimodal joint optimization model can accurately classify NPDR and PDR and precisely locate potential biomarkers.
With the development of society and the progress of technology, artificial intelligence (AI) and big data technology have penetrated into all walks of life in social production and promoted social production and lifestyle greatly. In the medical field, the applications of AI, such as AI-assisted diagnosis and treatment, robots, medical imaging and so on, have greatly promoted the development and transformation of the entire medical industry. At present, with the support of national policy, market, and technology, we should seize the opportunity of AI development, so as to build the first-mover advantage of AI development. Of course, the development and challenges are coexisted. In the future development process, we should objectively analyze the gap between our country and developed countries, think about the unfavorable factors such as AI chips and data problems, and extend the application and service of AI and big data to all links of medical industry, integrate with clinic fully, so as to better promote the further development of AI medicine treatment in China.
With the development of artificial intelligence, machine learning has been widely used in diagnosis of diseases. It is crucial to conduct diagnostic test accuracy studies and evaluate the performance of models reasonably to improve the accuracy of diagnosis. For machine learning-based diagnostic test accuracy studies, this paper introduces the principles of study design in the aspects of target conditions, selection of participants, diagnostic tests, reference standards and ethics.
Pathological myopia is one of the most challenging clinical diseases in the field of ophthalmology. The accurate definition, standard classification, disease evolution mechanism and disease prevention and treatment strategies are still under investigation. The development and application of artificial intelligence provides a powerful tool for the analysis of pathological myopia related data. More and more accurate data information is obtained in the clinical work and clinical research of pathological myopia through the standardized collection and acquisition of the fundus image data, the automatic segmentation and quantitative analysis of the fundus physiological structure, the automatic detection and analysis of the pathological myopia classic lesions and the clinical diagnosis and treatment decision aid, which helps ophthalmologists to understand the pathogenesis and evolution of pathological myopia.
With the development of computer technology, artificial intelligence (AI) has gradually been applied to various industries in society. In the healthcare industry, AI provides more choices for disease diagnosis and treatment, and also brings new vitality to the development of clinical medicine. In order to better promote the use of AI technology to improve the quality of otolaryngology teaching, this article provides a brief overview of the application of AI in otolaryngology, including the use of neural networks, deep learning for image analysis, disease diagnosis and treatment. It also discusses the significance and implementation methods of AI application in otolaryngology teaching from several aspects such as course design, teaching practice, and effectiveness assessment.
Objective To investigating the safety and accuracy of artificial intelligence (AI) assisted automatic planning of pedicle screws parallel to sagittal plane for C1. Methods The subjects who completed cervical CT scan in Zigong Fourth People’s Hospital btween January 2020 and December 2023 were selected. The subjects who completed cervical CT scan were randomly divided into two groups using a random number table method. Among them, 80% were used as the training model (training group), and 20% were used as the validation model (validation group). The original cervical CT data of the training group were imported into ITK-SNAP software to mark the feature points. Four feature points were selected. In order to obtain the weighted function model of the four feature points, training group were trained with the spatial key point location algorithm. pedicle trajectory based on the four key points obtained. Finally, the algorithm was compiled to form a visual interface, and imported into the verification group of annular vertebral CT data to calculate the pedicle screw trajectory. Results A total of 500 patients were included. Among them, there were 400 cases in the training group and 100 cases in the validation group. The average positioning error of spatial key points is (0.47±0.16) mm. The average distance between the planned pedicle screw center line and the internal edge of the pedicle was (2.86±0.12) mm. Pedicle screw placement parallel to the sagittal plane and 3D display can be safely performed for the C1 pedicle that is large enough to accommodate a 3.5 mm diameter screw without cortical breakthrough. Conclusions For pedicle screw planning parallel to the sagittal plane in C1, training based on the spatial positioning algorithm of anterior and posterior tubercles and bilateral tangential points can obtain a safe and accurate pedicle screw trajectory. It provides theoretical basis for orthopedic robot automatic screw placement. For vertebral bodies with narrow or deformed pedicles, further expansion of the training data is needed to expand the adaptive range and improve the accuracy of the algorithm.
China is facing the peak of an ageing population, and there is an increase in demand for intelligent healthcare services for the elderly. The metaverse, as a new internet social communication space, has shown infinite potential for application. This paper focuses on the application of the metaverse in medicine in the intervention of cognitive decline in the elderly population. The problems in assessment and intervention of cognitive decline in the elderly group were analyzed. The basic data required to construct the metaverse in medicine was introduced. Moreover, it is demonstrated that the elderly users can conduct self-monitoring, experience immersive self-healing and health-care through the metaverse in medicine technology. Furthermore, we proposed that it is feasible that the metaverse in medicine has obvious advantages in prediction and diagnosis, prevention and rehabilitation, as well as assisting patients with cognitive decline. Risks for its application were pointed out as well. The metaverse in medicine technology solves the problem of non-face-to-face social communication for elderly users, which may help to reconstruct the social medical system and service mode for the elderly population.
ObjectiveTo build a small-sample ultra-widefield fundus images (UWFI) multi-disease classification artificial intelligence model, and initially explore the ability of artificial intelligence to classify UWFI multi-disease tasks. MethodsA retrospective study. From 2016 to 2021, 1 608 images from 1 123 patients who attended the Eye Center of the Renmin Hospital of Wuhan University and underwent UWFI examination were used for UWFI multi-disease classification artificial intelligence model construction. Among them, 320, 330, 319, 268, and 371 images were used for diabetic retinopathy (DR), retinal vein occlusion (RVO), pathological myopia (PM), retinal detachment (RD), and normal fundus images, respectively. 135 images from 106 patients at the Tianjin Medical University Eye Hospital were used as the external test set. EfficientNet-B7 was selected as the backbone network for classification analysis of the included UWFI images. The performance of the UWFI multi-task classification model was assessed using the receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity, and accuracy. All data were expressed using numerical values and 95% confidence intervals (CI). The datasets were trained on the network models ResNet50 and ResNet101 and tested on an external test set to compare and observe the performance of EfficientNet with the 2 models mentioned above. ResultsThe overall classification accuracy of the UWFI multi-disease classification artificial intelligence model on the internal and external test sets was 92.57% (95%CI 91.13%-92.92%) and 88.89% (95%CI 88.11%-90.02%), respectively. These were 96.62% and 92.59% for normal fundus, 95.95% and 95.56% for DR, 96.62% and 98.52% for RVO, 98.65% and 97.04% for PM, and 97.30% and 94.07% for RD, respectively. The mean AUC on the internal and external test sets was 0.993 and 0.983, respectively, with 0.994 and 0.939 for normal fundus, 0.999 and 0.995 for DR, 0.985 and 1.000 for RVO, 0.991 and 0.993 for PM and 0.995 and 0.990 for RD, respectively. EfficientNet performed better than the ResNet50 and ResNet101 models on both the internal and external test sets. ConclusionThe preliminary UWFI multi-disease classification artificial intelligence model using small samples constructed in this study is able to achieve a high accuracy rate, and the model may have some value in assisting clinical screening and diagnosis.
With the rapid development of artificial intelligence (AI), especially deep learning, AI research in the field of ophthalmology has presented a trend of diversification in disease types, generalization in scenarios and deepening in researches. The AI algorithm has showed a good performance in the studies of diabetic retinopathy, age-related macular degeneration, glaucoma and other ocular diseases, yielding up the great potential of ophthalmic AI. However, most studies are still in their infancy, and the application of ophthalmic AI still faces many challenges such as lack of interpretability for results, deficiency of data standardization, and insufficiency of clinical applicability. At the same time, it should also be noted that the development of multi-modal imaging, the innovation of digital technologies (such as 5G and the Internet of Things) and telemedicine, and the new discovery that retina status can reflect systemic diseases have brought new opportunities for the development of ophthalmic AI. Learn the current status of AI research in the field of ophthalmology, grasp the new challenges and opportunities in its development process, successfully realizing the transformation of ophthalmic AI from research to practical application.
ObjectiveTo establish an artificial intelligence robot-assisted diagnosis system for fundus diseases based on deep learning optical coherence tomography (OCT) and evaluate its application value. MethodsDiagnostic test studies. From 2016 to 2019, 25 000 OCT images of 25 000 patients treated at the Eye Center of the Second Affiliated Hospital of Zhejiang University School of Medicine were used as training sets and validation sets for the fundus intelligent assisted diagnosis system. Among them, macular epiretinal membrane (MERM), macular edema, macular hole, choroidal neovascularization (CNV), and age-related macular degeneration (AMD) were 5 000 sheets each. The training set and the verification set are 18 124 and 6 876 sheets, respectively. Through the transfer learning Attention ResNet structure algorithm, the OCT image was characterized by lesion identification, the disease feature was extracted by a specific procedure, and the given image was distinguished from other types of disease according to the statistical characteristics of the target lesion. The model algorithms of MERM, macular edema, macular hole, CNV and AMD were initially formed, and the fundus intelligent auxiliary diagnosis system of five models was established. The performance of each model-assisted diagnosis in the fundus intelligent auxiliary diagnostic system was evaluated by applying the subject working characteristic curve, area under the curve (AUC), sensitivity, and specificity. ResultsWith the intelligent auxiliary diagnosis system, the diagnostic sensitivity of the MERM was 93.5%, the specificity was 99.23%, and AUC was 0.983 7; the diagnostic sensitivity of macular edema was 99.02%, the specificity was 98.17%, and AUC was 0.994 6; the diagnostic sensitivity of macular hole was 98.91%, the specificity was 99.91%, AUC was 0.996 2; the diagnostic sensitivity of CNV was 97.54%, the specificity was 94.71%, AUC was 0.987 5; the diagnostic sensitivity of AMD was 95.12%, the specificity was 97.09%, AUC was 0.985 3. ConclusionsThe artificial intelligence robot-assisted diagnosis system for fundus diseases based on deep learning for OCT images has accurate and efficient diagnostic performance for assisting the diagnosis of MERM, macular edema, macular hole, CNV, and AMD.
