Optical coherence tomography (OCT), as a high-resolution, non-invasive, in-vivo image method has been widely used in retinal field, especially in the examination of fundus diseases. Nowadays, the modality has been gradually popularized in most of the national basic-level hospitals. However, OCT is only employed as a diagnostic tool in most cases, ophthalmologists lack of awareness of further exploring the information behind the raw data. In the era of fast-developing artificial intelligence, on the basis of standardized information management, a more comprehensive OCT database should be established. Further original image processing, lesion analysis, and artificial intelligence development of OCT images will help improve the understanding level of vitreoretinal diseases among clinicians and assist ophthalmologists to make more appropriate clinical decisions.
ObjectiveTo evaluate the diagnostic value of artificial intelligence (AI)-assisted diagnostic system for pulmonary cancer based on CT images.MethodsDatabases including PubMed, The Cochrane Library, EMbase, CNKI, WanFang Data and Chinese BioMedical Literature Database (CBM) were electronically searched to collect relevant studies on AI-assisted diagnostic system in the diagnosis of pulmonary cancer from 2010 to 2019. The eligible studies were selected according to inclusion and exclusion criteria, and the quality of included studies was assessed and the special information was identified. Then, meta-analysis was performed using RevMan 5.3, Stata 12.0 and SAS 9.4 softwares. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio were pooled and the summary receiver operating characteristic (SROC) curve was drawn. Meta-regression analysis was used to explore the sources of heterogeneity.ResultsTotally 18 studies were included with 4 771 patients. Random effect model was used for the analysis due to the heterogeneity among studies. The results of meta-analysis showed that the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnosis odds ratio and area under the SROC curve were 0.87 [95%CI (0.84, 0.90)], 0.89 [95%CI (0.84, 0.92)], 7.70 [95%CI (5.32, 11.15)], 0.14 [95%CI (0.11, 0.19)], 53.54 [95%CI (30.68, 93.42)] and 0.94 [95%CI (0.91, 0.95)], respectively.ConclusionAI-assisted diagnostic system based on CT images has high diagnostic value for pulmonary cancer, and thus it is worthy of clinical application. However, due to the limited quality and quantity of included studies, above results should be validated by more studies.
For the past few years, artificial intelligence (AI) technology has developed rapidly and has become frontier and hot topics in medical research. While the deep learning algorithm based on artificial neural networks is one of the most representative tool in this field. The advancement of ophthalmology is inseparable from a variety of imaging methods, and the pronounced convenience and high efficiency endow AI technology with promising applications in screening, diagnosis and follow-up of ophthalmic diseases. At present, related research on ophthalmologic AI technology has been carried out in terms of multiple diseases and multimodality. Many valuable results have been reported aiming at several common diseases of ophthalmology. It should be emphasized that ophthalmic AI products are still faced with some problems towards practical application. The regulatory mechanism and evaluation criteria have not yet integrated as a standardized system. There are still a number of aspects to be optimized before large-scale distribution in clinical utility. Briefly, the innovation of ophthalmologic AI technology is attributed to multidisciplinary cooperation, which is of great significance to China's public health undertakings, and will be bound to benefit patients in future clinical practice.
Cardiovascular diseases are the leading cause of death and their diagnosis and treatment rely heavily on the variety of clinical data. With the advent of the era of medical big data, artificial intelligence (AI) has been widely applied in many aspects such as imaging, diagnosis and prognosis prediction in cardiovascular medicine, providing a new method for accurate diagnosis and treatment. This paper reviews the application of AI in cardiovascular medicine.
This review describes the concept of artificial intelligence, introduces the working mechanism and the main structure of medical expert system, as well as the development history of medical expert system at home and abroad and its applications in the medical field. The concept of machine learning, commonly used algorithms and its clinical applications in medical diagnosis are briefly described. It mainly introduces the application of artificial intelligence in neurology. The advantages and disadvantages of artificial intelligence system in medical field are analyzed. Finally, the future of artificial intelligence in the medical field is forecasted.
Artificial intelligence belongs to the field of computer science. In the past few decades, artificial intelligence has shown broad application prospects in the medical field. With the development of computer technology in recent years, doctors and computer scientists have just begun to discover its potential for clinical application, especially in the field of congenital heart disease. Artificial intelligence now has been successfully applied to the prediction, intelligent diagnosis, medical image segmentation and recognition, clinical decision support of congenital heart disease. This article reviews the application of artificial intelligence in congenital cardiology.
ObjectiveTo explore the application of artificial intelligence in postoperative follow-up of day surgery patients, so as to establish an intelligent medical framework, promote the intelligent process of hospitals, and improve the management level of day surgery.MethodsThe artificial intelligence phonetic system was carried out by the Day Surgery Center, Renji Hospital, Shanghai Jiaotong University School of Medicine on June 1st, 2018. Through the system, the artificial intelligence voice system based on speech and semantic recognition technology was adopted to connect the data of the information center in the hospital to carry out postoperative follow-up of day surgery patients. We selected the 2 245 patients followed up by the artificial intelligence phonetic system from June 1st to November 30th 2018 (the AI follow-up group) and the 2 576 patients followed up by the traditional manual method from January 2nd to May 31st 2018 (the manual follow-up group), to compare the telephone connection rate, information collection rate, and call duration between them.ResultsThere was no statistically significant difference in telephone connection rate (85.70% vs. 86.68%) or information collection rate (98.86% vs. 98.48%) between the AI follow-up group and the manual follow-up group (P>0.05); but there was a statistically significant difference in call duration between the AI follow-up group and the manual follow-up group [(165.48±43.28) vs. (135.37±36.31) seconds, P<0.05], and the AI follow-up group had a longer call duration.ConclusionsThe application of artificial intelligence phonetic system in surgery has a good performance in call connection rate and information collection integrity. It plays an active role in improving efficiency, extending medical services and strengthening medical safety in the management of day surgery.
Tuberculosis is one of the major infectious diseases that seriously endanger human health. Since 2014, it has surpassed human immunodeficiency virus/acquired immunodeficiency syndrome as the first infectious disease in patients with single pathogens. China is the third-largest country in the world in terms of high burden of tuberculosis. In 2016, there were about 900 000 new cases of tuberculosis in China. China is facing a severe tuberculosis epidemic, especially for the early diagnosis of tuberculosis and misdiagnosis of tuberculosis, which leads to delay in treatment and the spread of tuberculosis. With the application of artificial intelligence in the medical field, machine learning and deep learning methods have shown important value in the diagnosis of tuberculosis. This article will explain the application status and future development of machine learning and deep learning in the diagnosis of tuberculosis.
ObjectiveTo analyze the application effects of artificial intelligence (AI) software and Mimics software in preoperative three-dimensional (3D) reconstruction for thoracoscopic anatomical pulmonary segmentectomy. MethodsA retrospective analysis was conducted on patients who underwent thoracoscopic pulmonary segmentectomy at the Second People's Hospital of Huai'an from October 2019 to March 2024. Patients who underwent AI 3D reconstruction were included in the AI group, those who underwent Mimics 3D reconstruction were included in the Mimics group, and those who did not undergo 3D reconstruction were included in the control group. Perioperative related indicators of each group were compared. ResultsA total of 168 patients were included, including 73 males and 95 females, aged 25-81 (61.61±10.55) years. There were 79 patients in the AI group, 53 patients in the Mimics group, and 36 patients in the control group. There were no statistical differences in gender, age, smoking history, nodule size, number of lymph node dissection groups, postoperative pathological results, or postoperative complications among the three groups (P>0.05). There were statistical differences in operation time (P<0.001), extubation time (P<0.001), drainage volume (P<0.001), bleeding volume (P<0.001), and postoperative hospital stay (P=0.001) among the three groups. There were no statistical differences in operation time, extubation time, bleeding volume, or postoperative hospital stay between the AI group and the Mimics group (P>0.05). There was no statistical difference in drainage volume between the AI group and the control group (P=0.494), while there were statistical differences in operation time, drainage tube retention time, bleeding volume, and postoperative hospital stay (P<0.05). ConclusionFor patients requiring thoracoscopic anatomical pulmonary segmentectomy, preoperative 3D reconstruction and preoperative planning based on 3D images can shorten the operation time, postoperative extubation time and hospital stay, and reduce intraoperative bleeding and postoperative drainage volume compared with reading CT images only. The use of AI software for 3D reconstruction is not inferior to Mimics manual 3D reconstruction in terms of surgical guidance and postoperative recovery, which can reduce the workload of clinicians and is worth promoting.
Objective To explore the application value of artificial intelligence (AI) pulmonary artery assisted diagnosis software for suspected pulmonary embolism patients. Methods The data of 199 patients who were clinically suspected of pulmonary embolism and underwent pulmonary artery CT angiography (CTA) from June 2016 to December 2021 were retrospectively analyzed. Images of pulmonary artery CTA diagnosed by radiologists with different experiences and judged by senior radiologists were compared with the analysis results of AI assisted diagnostic software for pulmonary artery CTA, to evaluate the diagnostic efficacy of this software and low, medium, and senior radiologists for pulmonary embolism. The agreement of pulmonary embolism based on pulmonary artery CTA between the AI software and radiologists with different experiences was evaluated using Kappa test. Results The agreement of the AI software and the evaluation of pulmonary embolism lesions by senior radiologists based on pulmonary artery CTA was high (Kappa=0.913, P<0.001), while the diagnostic results of pulmonary artery CTA AI software was good after judged by senior radiologists based on pulmonary artery CTA (Kappa=0.755, P<0.001). Conclusions The AI software based on pulmonary artery CTA diagnosis of pulmonary embolism has good consistency with diagnostic images of radilogists, and can save a lot of reconstruction and diagnostic time. It has the value of daily diagnosis work and worthy of clinical promotion.