At present, artificial intelligence (AI) has been widely used in the diagnosis and treatment of various ophthalmological diseases, but there are still many problems. Due to the lack of standardized test sets, gold standards, and recognized evaluation systems for the accuracy of AI products, it is difficult to compare the results of multiple studies. When it comes to the field of image generation, we hardly have an efficient approach to evaluating research results. In clinical practice, ophthalmological AI research is often out of touch with actual clinical needs. The requirements for the quality and quantity of clinical data put more burden on AI research, limiting the transformation of AI studies. The prediction of systemic diseases based on fundus images is making progressive advancement. However, the lack of interpretability of the research lower the acceptance. Ophthalmology AI research also suffer from ethical controversy due to unconstructed regulations and regulatory mechanisms, concerns on patients’ privacy and data security, and the risk of aggravating the unfairness of medical resources.
Objective To systematically evaluate the accuracy of endoscopy-based artificial intelligence (AI)-assisted diagnostic systems in the diagnosis of early-stage esophageal cancer and provide a scientific basis for its diagnostic value. MethodsPubMed, EMbase, The Cochrane Library, Web of Science, Wanfang database, VIP database and CNKI database were searched by computer to search for the relevant literature about endoscopy-based AI-assisted diagnostic systems for the diagnosis of early esophageal cancer from inception to March 2022. The QUADAS-2 was used for quality evaluation of included studies. Meta-analysis of the literature was carried out using Stata 16, Meta-Disc 1.4 and RevMan 5.4 softwares. A bivariate mixed effects regression model was utilized to calculate the combined diagnostic efficacy of the AI-assisted system and meta-regression analysis was conducted to explore the sources of heterogeneity. ResultsA total of 17 articles were included, which consisted of 13 retrospective cohort studies and 4 prospective cohort studies. The results of the quality evaluation using QUADAS-2 showed that all included literature was of high quality. The obtained meta-analysis results revealed that the AI-assisted system in the diagnosis of esophageal cancer presented a combined sensitivity of 0.94 (95%CI 0.91 to 0.96), a specificity of 0.85 (95%CI 0.74 to 0.92), a positive likelihood ratio of 6.28 (95%CI 3.48 to 11.33), a negative likelihood ratio of 0.07 (95%CI 0.05 to 0.11), a diagnostic odds ratio of 89 (95%CI 38 to 208) and an area under the curve of 0.96 (95%CI 0.94 to 0.98). ConclusionThe AI-assisted diagnostic system has a high diagnostic value for early stage esophageal cancer. However, most of the included studies were retrospective. Therefore, further high-quality prospective studies are needed for validation.
Objective To develop a neural network architecture based on deep learning to assist knee CT images automatic segmentation, and validate its accuracy. Methods A knee CT scans database was established, and the bony structure was manually annotated. A deep learning neural network architecture was developed independently, and the labeled database was used to train and test the neural network. Metrics of Dice coefficient, average surface distance (ASD), and Hausdorff distance (HD) were calculated to evaluate the accuracy of the neural network. The time of automatic segmentation and manual segmentation was compared. Five orthopedic experts were invited to score the automatic and manual segmentation results using Likert scale and the scores of the two methods were compared. Results The automatic segmentation achieved a high accuracy. The Dice coefficient, ASD, and HD of the femur were 0.953±0.037, (0.076±0.048) mm, and (3.101±0.726) mm, respectively; and those of the tibia were 0.950±0.092, (0.083±0.101) mm, and (2.984±0.740) mm, respectively. The time of automatic segmentation was significantly shorter than that of manual segmentation [(2.46±0.45) minutes vs. (64.73±17.07) minutes; t=36.474, P<0.001). The clinical scores of the femur were 4.3±0.3 in the automatic segmentation group and 4.4±0.2 in the manual segmentation group, and the scores of the tibia were 4.5±0.2 and 4.5±0.3, respectively. There was no significant difference between the two groups (t=1.753, P=0.085; t=0.318, P=0.752). Conclusion The automatic segmentation of knee CT images based on deep learning has high accuracy and can achieve rapid segmentation and three-dimensional reconstruction. This method will promote the development of new technology-assisted techniques in total knee arthroplasty.
ObjectiveTo investigate the early effectiveness of artificial intelligence (AI) assisted total hip arthroplasty (THA) system (AIHIP) in the treatment of patients with Crowe type Ⅳ developmental dysplasia of the hip (DDH).MethodsThe clinical data of 23 patients with Crowe type Ⅳ DDH who met the selection criteria between May 2019 and December 2020 were retrospectively analyzed. There were 3 males and 20 females, the age ranged from 44 to 74 years, with an average of 52.65 years. The absolute value of the lower limbs discrepancy before operation was (15.17±22.17) mm. The preoperative Harris score was 62.4±7.2. The AIHIP system was used for preoperative planning, and the operations were all performed via conventional posterolateral approach. Thirteen patients with difficulty in reduction during operation underwent subtrochanteric shortening osteotomy (SSOT). The operation time, hospital stay, and adverse events were recorded; Harris score was used to evaluate the function of the affected limb at 1 day before operation and 1 week and 6 months after operation; pelvic anteroposterior X-ray film was taken at 1 day after operation to evaluate the position of the prosthesis. The matching degree of prosthesis was evaluated according to the consistency of intraoperative prosthesis model and preoperative planning.ResultsThe matching degree of acetabular cup model after operation was 16 cases of perfect matching, 4 cases of general matching (1 case of +1, 3 cases of –1), and 3 cases of mismatch (all of them were +2), the coincidence rate was 86.96%. The matching degree of femoral stem model was perfect matching in 22 cases and general matching in 1 case of –1, and the coincidence rate was 100%. One patient had a periprosthesis fracture during operation, and was fixed with a wire cable during operation, and walked with the assistance of walking aid at 6 weeks after operation; the rest of the patients walked with the assistance of walking aid at 1 day after operation. The operation time was 185-315 minutes, with an average of 239.43 minutes; the hospital stay was 8-20 days, with an average of 9.96 days; and the time of disengagement from the walking aid was 2-56 days, with an average of 5.09 days. All patients were followed up 6 months. All incisions healed by first intension, and there was no complication such as infection, dislocation, refracture, and lower extremity deep venous thrombosis; X-ray films at 1 day and 6 months after operation showed that the acetabular and femoral prostheses were firmly fixed and within the safe zone; the absolute value of lower limbs discrepancy at 1 day after operation was (11.96±13.48) mm, which was not significantly decreased compared with that before operation (t=0.582, P=0.564). All osteotomies healed at 6 months after operation. The Harris scores at 1 week and 6 months after operation were 69.5±4.9 and 79.2±5.7 respectively, showing significant differences between pre- and post-operation (P<0.05). At 6 months after operation, the hip function was evaluated according to Harris score, and 13 cases were good, 9 cases were fair, and 1 case was poor.ConclusionAIHIP system-assisted THA (difficult to reposition patients combined with SSOT) for adult Crowe type Ⅳ DDH has high preoperative planning accuracy, easy intraoperative reduction, early postoperative landing, and satisfactory short-term effectiveness.
Retinopathy of prematurity (ROP) is a major cause of vision loss and blindness among premature infants. Timely screening, diagnosis, and intervention can effectively prevent the deterioration of ROP. However, there are several challenges in ROP diagnosis globally, including high subjectivity, low screening efficiency, regional disparities in screening coverage, and severe shortage of pediatric ophthalmologists. The application of artificial intelligence (AI) as an assistive tool for diagnosis or an automated method for ROP diagnosis can improve the efficiency and objectivity of ROP diagnosis, expand screening coverage, and enable automated screening and quantified diagnostic results. In the global environment that emphasizes the development and application of medical imaging AI, developing more accurate diagnostic networks, exploring more effective AI-assisted diagnosis methods, and enhancing the interpretability of AI-assisted diagnosis, can accelerate the improvement of AI policies of ROP and the implementation of AI products, promoting the development of ROP diagnosis and treatment.
With the rapid development of artificial intelligence (AI) technology, its application in hospital management is gradually becoming an important means to improve operational efficiency and the quality of patient health care. This article systematically explores the multidimensional applications of AI in hospital management, including medical services, administration, patient engagement and experience. Through in-depth analysis, the paper evaluates the potential of AI in these areas, especially the significant effect in improving operational efficiency and optimising patient healthcare services. However, the application of AI also faces many challenges, such as data privacy issues, algorithmic bias, operational management, and economic factors. This article not only identifies these challenges, but also provides specific inspiration and recommendations for hospital management in China, emphasises the importance of adaptability and continuous learning, and calls on hospital administrators to actively embrace change in order to achieve both improved patient health outcomes and operational efficiency.
ObjectiveTo compare the consistency of artificial analysis and artificial intelligence analysis in the identification of fundus lesions in diabetic patients.MethodsA retrospective study. From May 2018 to May 2019, 1053 consecutive diabetic patients (2106 eyes) of the endocrinology department of the First Affiliated Hospital of Zhengzhou University were included in the study. Among them, 888 patients were males and 165 were females. They were 20-70 years old, with an average age of 53 years old. All patients were performed fundus imaging on diabetic Inspection by useing Japanese Kowa non-mydriatic fundus cameras. The artificial intelligence analysis of Shanggong's ophthalmology cloud network screening platform automatically detected diabetic retinopathy (DR) such as exudation, bleeding, and microaneurysms, and automatically classifies the image detection results according to the DR international staging standard. Manual analysis was performed by two attending physicians and reviewed by the chief physician to ensure the accuracy of manual analysis. When differences appeared between the analysis results of the two analysis methods, the manual analysis results shall be used as the standard. Consistency rate were calculated and compared. Consistency rate = (number of eyes with the same diagnosis result/total number of effective eyes collected) × 100%. Kappa consistency test was performed on the results of manual analysis and artificial intelligence analysis, 0.0≤κ＜0.2 was a very poor degree of consistency, 0.2≤κ＜0.4 meant poor consistency, 0.4≤κ＜0.6 meant medium consistency, and 0.6≤κ＜1.0 meant good consistency.ResultsAmong the 2106 eyes, 64 eyes were excluded that cannot be identified by artificial intelligence due to serious illness, 2042 eyes were finally included in the analysis. The results of artificial analysis and artificial intelligence analysis were completely consistent with 1835 eyes, accounting for 89.86%. There were differences in analysis of 207 eyes, accounting for 10.14%. The main differences between the two are as follows: (1) Artificial intelligence analysis points Bleeding, oozing, and manual analysis of 96 eyes (96/2042, 4.70%); (2) Artificial intelligence analysis of drusen, and manual analysis of 71 eyes (71/2042, 3.48%); (3) Artificial intelligence analyzes normal or vitreous degeneration, while manual analysis of punctate exudation or hemorrhage or microaneurysms in 40 eyes (40/2042, 1.95%). The diagnostic rates for non-DR were 23.2% and 20.2%, respectively. The diagnostic rates for non-DR were 76.8% and 79.8%, respectively. The accuracy of artificial intelligence interpretation is 87.8%. The results of the Kappa consistency test showed that the diagnostic results of manual analysis and artificial intelligence analysis were moderately consistent (κ=0.576, P＜0.01).ConclusionsManual analysis and artificial intelligence analysis showed moderate consistency in the diagnosis of fundus lesions in diabetic patients. The accuracy of artificial intelligence interpretation is 87.8%.
The rapid development of medical imaging methods based on artificial intelligence (AI) has led to the first release of the AI medical imaging research checklist (CLAIM) in 2020 to promote the completeness and consistency of AI medical imaging research reports. However, during the application process, it was found that some entries in CLAIM needed improvement. Therefore, the expert committee updated CLAIM and released the updated version of CLAIM 2024. This article introduces CLAIM 2024 for domestic scholars to follow up and refer to in a timely manner.
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 By comparing with the traditional X-ray template measurement method, to explore the accuracy of artificial intelligence preoperative planning system (AI-HIP) to predict the type of prosthesis and guide the placement of prosthesis before total hip arthroplasty (THA) in adult patients with developmental dysplasia of the hip (DDH). Methods Patients with DDH scheduled for initial THA between August 2020 and August 2022 were enrolled as study object, of which 28 cases (28 hips) met the selection criteria were enrolled in the study. Among them, there were 10 males and 18 females, aged from 34 to 77 years, with an average of 59.3 years. There were 12 cases of the left DDH and 16 cases of the right DDH. According to DDH classification, there were 10 cases of Crowe type Ⅰ, 8 cases of type Ⅱ, 5 cases of type Ⅲ, and 5 cases of type Ⅳ. According to Association Research Circulation Osseous (ARCO) staging of osteonecrosis of the femoral head, 13 cases were in stage Ⅲ and 15 cases in stage Ⅳ. The disease duration was 2.5-23.0 years (mean, 8.6 years). The limb length discrepancy (LLD) was 11.0 (8.0, 17.5) mm. Before operation, the prosthesis types of all patients were predicted by AI-HIP system and X-ray template measurement method, respectively. And the preoperative results were compared with the actual prosthesis type during operation in order to estimate the accuracy of the AI-HIP system. Then, the differences in the acetabular abduction angle, acetabular anteversion angle, femoral neck osteotomy position, tip-shoulder distance, and LLD were compared between preoperative planned measurements by AI-HIP system and actual measurement results after operation, in order to investigate the ability of AI-HIP system to evaluate the placement position of prosthesis. Results The types of acetabular and femoral prostheses predicted based on AI-HIP system before operation were consistent with the actual prostheses in 23 cases (82.1%) and 24 cases (85.7%), respectively. The types of acetabular and femoral prostheses predicted based on X-ray template measurement before operation were consistent with the actual prostheses in 16 cases (57.1%) and 17 cases (60.7%), respectively. There were significant differences between AI-HIP system and X-ray template measurement (P<0.05). There was no significant difference in acetabular abduction angle, acetabular anteversion angle, femoral neck osteotomy position, and tip-shoulder distance between AI-HIP system and actual measurement after operation (P>0.05). LLD after operation was significantly lower than that before operation (P<0.05). There was no significant difference between the LLD predicted based on AI-HIP system and the actual measurement after operation (P>0.05). Conclusion Compared with the traditional X-ray template measurement method, the preoperative planning of AI-HIP system has better accuracy and repeatability in predicting the prosthesis type. It has a certain reference for the prosthesis placement of adult DDH.