Influence of clinical and experimental intra-articular inflammation on neutrophil gelatinase-associated lipocalin concentrations in horses.

OBJECTIVE: To investigate neutrophil gelatinase-associated lipocalin (NGAL) concentrations in serum and synovial fluid (SF) from horses with joint inflammation. STUDY DESIGN: Experimental studies and retrospective clinical study. SAMPLE POPULATION: Serum and SF samples were available from healthy horses (n = 19), clinical cases, and horses with experimental joint inflammation. Clinical cases included horses with (n = 10) or without (n = 10) septic arthritis. Experimental intra-articular inflammation was induced by lipopolysaccharide (LPS; n = 7, severe inflammation), lidocaine (n = 6, moderate inflammation), or mepivacaine (n = 6, mild inflammation). METHODS: Availability of samples was based on approval from the local ethical committee and from the Danish Animal Experiments Inspectorate. Neutrophil gelatinase-associated lipocalin was measured with a previously validated enzyme-linked immunosorbent assay. Repeated-measurements one- and two-way analysis of variance and correlation analysis were used to analyze NGAL concentrations and white blood cell counts (WBC). RESULTS: After injection of LPS or lidocaine, SF NGAL concentrations increased 343- (P = .0035) and 60-fold (P = .0038) relative to baseline, respectively. Serum NGAL also increased in both groups (P < .05) but to lower concentrations than in SF. Concentrations were higher after injection of lidocaine SF NGAL than after injection of mepivacaine (P < .05) at 6 and 12 hours. Synovial fluid concentrations of NGAL were higher in horses with septic arthritis than in the nonseptic group (P = .0070) and in healthy controls (P = .0071). Concentrations of NGAL correlated with WBC in SF (P < .0001, R2 = 0.49) and in blood (P = .0051, R2 = 0.27). CONCLUSION: Neutrophil gelatinase-associated lipocalin concentrations increased in SF in response to experimentally induced and naturally occurring joint inflammation. Synovial fluid NGAL concentration correlated with WBC and, thus, seems to reflect intensity of joint inflammation. CLINICAL SIGNIFICANCE: Neutrophil gelatinase-associated lipocalin may prove to be a useful biomarker of joint inflammation and infection in horses.

Deep Learning-Based Algorithms in Screening of Diabetic Retinopathy: A Systematic Review of Diagnostic Performance.

TOPIC: Diagnostic performance of deep learning-based algorithms in screening patients with diabetes for diabetic retinopathy (DR). The algorithms were compared with the current gold standard of classification by human specialists. CLINICAL RELEVANCE: Because DR is a common cause of visual impairment, screening is indicated to avoid irreversible vision loss. Automated DR classification using deep learning may be a suitable new screening tool that could improve diagnostic performance and reduce manpower. METHODS: For this systematic review, we aimed to identify studies that incorporated the use of deep learning in classifying full-scale DR in retinal fundus images of patients with diabetes. The studies had to provide a DR grading scale, a human grader as a reference standard, and a deep learning performance score. A systematic search on April 5, 2018, through MEDLINE and Embase yielded 304 publications. To identify potentially missed publications, the reference lists of the final included studies were manually screened, yielding no additional publications. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used for risk of bias and applicability assessment. RESULTS: By using objective selection, we included 11 diagnostic accuracy studies that validated the performance of their deep learning method using a new group of patients or retrospective datasets. Eight studies reported sensitivity and specificity of 80.28% to 100.0% and 84.0% to 99.0%, respectively. Two studies report accuracies of 78.7% and 81.0%. One study provides an area under the receiver operating curve of 0.955. In addition to diagnostic performance, one study also reported on patient satisfaction, showing that 78% of patients preferred an automated deep learning model over manual human grading. CONCLUSIONS: Advantages of implementing deep learning-based algorithms in DR screening include reduction in manpower, cost of screening, and issues relating to intragrader and intergrader variability. However, limitations that may hinder such an implementation particularly revolve around ethical concerns regarding lack of trust in the diagnostic accuracy of computers. Considering both strengths and limitations, as well as the high performance of deep learning-based algorithms, automated DR classification using deep learning could be feasible in a real-world screening scenario.