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Background: Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method.
Methods: Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and gender-specific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method.
Results: Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years; p < 0.001). The correlation between AI-derived BA and reference BA (r = 0.99) was significantly higher than between reader-calculated BA and reference BA (r = 0.90; p < 0.001). No statistical difference was found in reader agreement and correlation analyses regarding gender (p = 0.241). Mean reading times were reduced by 87% using the AI system.
Conclusions: A novel AI software enabled highly accurate automated BA assessment. It may improve efficiency in clinical routine by reducing reading times without compromising the accuracy compared with the Greulich-Pyle method.
We sought to determine the effects of the use of a Bioengineered Combo Dual-Therapy CD34 Antibody-Covered Sirolimus-Eluting Coronary Stent (Combo® DTS) in patients with chronic total occlusion (CTO) by evaluating clinical outcomes and by performing an optical coherence tomography (OCT) analysis. We retrospectively analyzed data from 39 patients who had successfully undergone OCT-guided revascularization of a CTO being treated with a Combo® DTS. Clinical assessment, angiography (with quantitative coronary angiography analysis) and OCT examination were performed at baseline and at follow-up. The median follow-up period was 189 days, ranging from 157 to 615 days. At follow-up, revascularization was required due to angiographic restenosis in 40% (14 of 35) of patients. OCT analysis detected neointima proliferation in 23 (76.6%) patients. Neointima formation was often associated with microvessels in 18 patients (60%). Neoatheroslcerosis was observed in 2 (6.6%) patients. Malapposition was found in 4 patients (13.3%), and stent fractures were found in 11 patients (36.6%). Rate of strut coverage was 96.3% at follow-up. In conclusion, the implantation of a Combo® DTS after successful CTO recanalization was associated with a restenosis rate of 40% despite good stent implantation at baseline, proven by OCT. Neointima formation was found as a main contributor to restenosis. Nevertheless, we observed a low rate of major cardiovascular events in our follow-up.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.