Clinical efficacy of blood derivatives on wound healing: A systematic review and network meta‐analysis

Abstract

This study aims to evaluate the clinical effects of different blood derivatives on wound healing using network meta-analysis. PubMed, Embase, OVID, Web of Science, SCOPUS and Cochrane Central were searched to obtain studies about blood derivatives on wound healing until October 2023. R 4.2.0 and Stata 15.0 softwares were used for data analysis. Forty-four studies comprising 5164 patients were included. The results of network meta-analysis showed that the healing area from high to low was GF + ORCCB, ORCCB, GF, PRF, Unnas paste dressing, APG, PRP injection, PRP, PRP + thrombin gel, PPP, HPL, CT. The healing time from low to high was PRP + thrombin gel, GF, PRP, PC + K, PC, APG, PRF, CT, Silver sulfadiazine ointment. The number of patients cured from high to low was APG, PRP injection, PRP, Aurix, PRF, Leucopatch, HPL, Antimicrobial Ointment Dressing, CT, 60 μg/cm² repifermin, 120 μg/cm² repifermin, AFG, PPP. The order of analgesic effect from high to low was AFG, Aminogam gel, PRF, PRP, Oxidised oil, APG, GF, CT. The order of the number of wound infection cases from low to high is APG, 20 μg/cm² repifermin, 60 μg/cm² repifermin, PRP, LeucoPatch, CT, PPP, Antiseptic ointment dressing. Healing area: GF + ORCCB had the best effect; Healing time: PRP + thrombin gel took the shortest time. The number of cured patients and the reduction of wound infection: APG has the best effect. Analgesic effect: AFG has the best effect. More studies with large sample sizes are needed to confirm the above findings.

Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis

by Hangbin Zhang, Xinyu Yang, Ye Tao, Xinyi Zhang, Xuan Huang

Background

Endocytoscopy (EC) is a nuclei and micro-vessels visualization in real-time and can facilitate "optical biopsy" and "virtual histology" of colorectal lesions. This study aimed to investigate the significance of employing artificial intelligence (AI) in the field of endoscopy, specifically in diagnosing colorectal lesions. The research was conducted under the supervision of experienced professionals and trainees.

Methods

EMBASE, PubMed, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI) database, and other potential databases were surveyed for articles related to the EC with AI published before September 2023. RevMan (5.40), Stata (14.0), and R software (4.1.0) were used for statistical assessment. Studies that measured the accuracy of EC using AI for colorectal lesions were included. Two authors independently assessed the selected studies and their extracted data. This included information such as the country, literature, total study population, study design, characteristics of the fundamental study and control groups, sensitivity, number of samples, assay methodology, specificity, true positives or negatives, and false positives or negatives. The diagnostic accuracy of EC by AI was determined by a bivariate random-effects model, avoiding a high heterogeneity effect. The ANOVA model was employed to determine the more effective approach.

Results

A total of 223 studies were reviewed; 8 articles were selected that included 2984 patients (4241 lesions) for systematic review and meta-analysis. AI assessed 4069 lesions; experts diagnosed 3165 and 5014 by trainees. AI demonstrated high accuracy, sensitivity, and specificity levels in detecting colorectal lesions, with values of 0.93 (95% CI: 0.90, 0.95) and 0.94 (95% CI: 0.73, 0.99). Expert diagnosis was 0.90 (95% CI: 0.85, 0.94), 0.87 (95% CI: 0.78, 0.93), and trainee diagnosis was 0.74 (95% CI: 0.67, 0.79), 0.72 (95% CI: 0.62, 0.80). With the EC by AI, the AUC from SROC was 0.95 (95% CI: 0.93, 0.97), therefore classified as excellent category, expert showed 0.95 (95% CI: 0.93, 0.97), and the trainee had 0.79 (95% CI: 0.75, 0.82). The superior index from the ANOVA model was 4.00 (1.15,5.00), 2.00 (1.15,5.00), and 0.20 (0.20,0.20), respectively. The examiners conducted meta-regression and subgroup analyses to evaluate the presence of heterogeneity. The findings of these investigations suggest that the utilization of NBI technology was correlated with variability in sensitivity and specificity. There was a lack of solid evidence indicating the presence of publishing bias.

Conclusions

The present findings indicate that using AI in EC can potentially enhance the efficiency of diagnosing colorectal abnormalities. As a valuable instrument, it can enhance prognostic outcomes in ordinary EC procedures, exhibiting superior diagnostic accuracy compared to trainee-level endoscopists and demonstrating comparability to expert endoscopists. The research is subject to certain constraints, namely a limited number of clinical investigations and variations in the methodologies used for identification. Consequently, it is imperative to conduct comprehensive and extensive research to enhance the precision of diagnostic procedures.