Background/Aims: We have been developing artificial intelligence based polyp histology prediction (AIPHP) method to classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the hyperplastic or neoplastic histology of polyps. Our aim was to analyze the accuracy of AIPHP and narrow-band imaging international colorectal endoscopic (NICE) classification based histology predictions and also to compare the results of the two methods. Methods: We studied 373 colorectal polyp samples taken by polypectomy from 279 patients. The documented NBI still images were analyzed by the AIPHP method and by the NICE classification parallel. The AIPHP software was created by machine learning method. The software measures five geometrical and color features on the endoscopic image. Results: The accuracy of AIPHP was 86.6% (323/373) in total of polyps. We compared the AIPHP accuracy results for diminutive and non-diminutive polyps (82.1% vs. 92.2%; p=0.0032). The accuracy of the hyperplastic histology prediction was significantly better by NICE compared to AIPHP method both in the diminutive polyps (n=207) (95.2% vs. 82.1%) (p<0.001) and also in all evaluated polyps (n=373) (97.1% vs. 86.6%) (p<0.001) Conclusions Our artificial intelligence based polyp histology prediction software could predict histology with high accuracy only in the large size polyp subgroup.

PURPOSE: Based on a previous gene expression study in a mouse model of asthma, we selected 60 candidate genes and investigated their possible roles in human asthma. METHODS: In these candidate genes, 90 SNPs were genotyped using MassARRAY technology from 311 asthmatic children and 360 healthy controls of the Hungarian (Caucasian) population. Moreover, gene expression levels were measured by RT PCR in the induced sputum of 13 asthmatics and 10 control individuals. t-tests, chi-square tests, and logistic regression were carried out in order to assess associations of SNP frequency and expression level with asthma. Permutation tests were performed to account for multiple hypothesis testing. RESULTS: The frequency of 4 SNPs in 2 genes differed significantly between asthmatic and control subjects: SNPs rs2240572, rs2240571, rs3735222 in gene SCIN, and rs32588 in gene PPARGC1B. Carriers of the minor alleles had reduced risk of asthma with an odds ratio of 0.64 (0.51-0.80; P=7x10(-5)) in SCIN and 0.56 (0.42-0.76; P=1.2x10(-4)) in PPARGC1B. The expression levels of SCIN, PPARGC1B and ITLN1 genes were significantly lower in the sputum of asthmatics. CONCLUSIONS: Three potentially novel asthma-associated genes were identified based on mouse experiments and human studies.
Alleles ; Animals ; Asthma* ; Child ; Gene Expression ; Humans ; Inflammation* ; Logistic Models ; Mice* ; Odds Ratio ; Polymerase Chain Reaction ; Polymorphism, Single Nucleotide ; Sputum