No abstract

No abstract

ImportanceOne of the biggest challenges when using anti–vascular endothelial growth factor (VEGF) agents to treat retinopathy of prematurity (ROP) is the need to perform long-term follow-up examinations to identify eyes at risk of ROP reactivation requiring retreatment.ObjectiveTo evaluate whether an artificial intelligence (AI)–based vascular severity score (VSS) can be used to analyze ROP regression and reactivation after anti-VEGF treatment and potentially identify eyes at risk of ROP reactivation requiring retreatment.Design, Setting, and ParticipantsThis prognostic study was a secondary analysis of posterior pole fundus images collected during the multicenter, double-blind, investigator-initiated Comparing Alternative Ranibizumab Dosages for Safety and Efficacy in Retinopathy of Prematurity (CARE-ROP) randomized clinical trial, which compared 2 different doses of ranibizumab (0.12 mg vs 0.20 mg) for the treatment of ROP. The CARE-ROP trial screened and enrolled infants between September 5, 2014, and July 14, 2016. A total of 1046 wide-angle fundus images obtained from 19 infants at predefined study time points were analyzed. The analyses of VSS were performed between January 20, 2021, and November 18, 2022.InterventionsAn AI-based algorithm assigned a VSS between 1 (normal) and 9 (most severe) to fundus images.Main Outcomes and MeasuresAnalysis of VSS in infants with ROP over time and VSS comparisons between the 2 treatment groups (0.12 mg vs 0.20 mg of ranibizumab) and between infants who did and did not receive retreatment for ROP reactivation.ResultsAmong 19 infants with ROP in the CARE-ROP randomized clinical trial, the median (range) postmenstrual age at first treatment was 36.4 (34.7-39.7) weeks; 10 infants (52.6%) were male, and 18 (94.7%) were White. The mean (SD) VSS was 6.7 (1.9) at baseline and significantly decreased to 2.7 (1.9) at week 1 (P < .001) and 2.9 (1.3) at week 4 (P < .001). The mean (SD) VSS of infants with ROP reactivation requiring retreatment was 6.5 (1.9) at the time of retreatment, which was significantly higher than the VSS at week 4 (P < .001). No significant difference was found in VSS between the 2 treatment groups, but the change in VSS between baseline and week 1 was higher for infants who later required retreatment (mean [SD], 7.8 [1.3] at baseline vs 1.7 [0.7] at week 1) vs infants who did not (mean [SD], 6.4 [1.9] at baseline vs 3.0 [2.0] at week 1). In eyes requiring retreatment, higher baseline VSS was correlated with earlier time of retreatment (Pearson r = −0.9997; P < .001).Conclusions and RelevanceIn this study, VSS decreased after ranibizumab treatment, consistent with clinical disease regression. In cases of ROP reactivation requiring retreatment, VSS increased again to values comparable with baseline values. In addition, a greater change in VSS during the first week after initial treatment was found to be associated with a higher risk of later ROP reactivation, and high baseline VSS was correlated with earlier retreatment. These findings may have implications for monitoring ROP regression and reactivation after anti-VEGF treatment.

No abstract

Cold medical gas plasmas are under pre-clinical investigation concerning their hemostatic activity and could be applied for intra-operative bleeding control in the future. The technological leap innovation was their generation at body temperature, thereby causing no thermal harm to the tissue and ensuring tissue integrity. This directly contrasts with current techniques such as electrocautery, which induces hemostasis by carbonizing the tissue using a heated electrode. However, the necrotized tissue is prone to fall, raising the risk of post-operative complications such as secondary bleedings or infection. In recent years, various studies have reported on the ability of medical gas plasmas to induce blood coagulation, including several suggestions concerning their mode of action. As non-invasive and gentle hemostatic agents, medical gas plasmas could be particularly eligible for vulnerable tissues, e.g., colorectal surgery and neurosurgery. Further, their usage could be beneficial regarding the prevention of post-operative bleedings due to the absence or sloughing of eschar. However, no clinical trials or individual healing attempts for medical gas plasmas have been reported to pave the way for clinical approvement until now, despite promising results in experimental animal models. In this light, the present mini-review aims to emphasize the potential of medical gas plasmas to serve as a hemostatic agent in clinical procedures. Providing a detailed overview of the current state of knowledge, feasible application fields are discussed, and possible obstacles are addressed.

Diabetic retinopathy (DR) is one of the most common causes of blindness in adults (1-3). The global prevalence of DR is estimated to be 34.6 % in all diabetics (4). Early stages of DR can be asymptomatic for many years. The detection of DR is important to reduce the progression of DR and preserve vision in the long term (2). In recent years, a number of procedures have evolved that use fundus photographs to make a diagnostic determination of DR (5,6). In this issue of Annals of Translational Medicine, Yang and colleagues present findings of a deeplearning algorithm (artificial intelligence DR screening, AIDRScreening system) to detect DR from a prospective, multicenter clinical evaluation study (7). First, Yang's results are compared with results from existing algorithms. Also, international experiences from deep learning-based screening of DR are addressed. Trends in the field are highlighted and the unique situation in China is discussed in the article.The first deep learning method to be approved by the U.S. Food and Drug Administration (FDA) was the IDx-DR algorithm in 2018 (IDx Technologies Inc., Coralville, Iowa, USA) (8). Also, Google has developed an algorithm for DR detection: automated retinal disease assessment (ARDA) (9).All three algorithms (IDx-DR, ARDA and the novel AIDRScreening algorithm published in this issue of Annals of Translational Medicine) use convolutional-neural-networks as the main method for image classification. In all three