Refine
Year of publication
- 2020 (3) (remove)
Document Type
- Article (3)
Language
- English (3)
Has Fulltext
- yes (3)
Is part of the Bibliography
- no (3)
Keywords
Institute
- Medizin (3) (remove)
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.
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.
Introduction: Recommendations for venous thromboembolism and deep venous thrombosis (DVT) prophylaxis using graduated compression stockings (GCS) is historically based and has been critically examined in current publications. Existing guidelines are inconclusive as to recommend the general use of GCS.
Patients/Methods: 24 273 in-patients (general surgery and orthopedic patients) undergoing surgery between 2006 and 2016 were included in a retrospectively analysis from a single center. From January 2006 to January 2011 perioperative GCS was employed additionally to drug prophylaxis and from February 2011 to March 2016 patients received drug prophylaxis alone. According to german guidelines all patients received venous thromboembolism prophylaxis with weight-adapted LMWH. Risk stratification (low risk, moderate risk, high risk) was based on the guideline of the American College of Chest Physicians. Data analysis was performed before and after propensity matching (PM). The defined primary endpoint was the incidence of symptomatic or fatal pulmonary embolism (PE). A secondary endpoint was the incidence of deep venous thromboembolism (DVT).
Results: After risk stratification (low risk n = 16 483; moderate risk n = 4464; high risk n = 3326) a total of 24 273 patient were analyzed. Before to PM the relative risk for the occurrence of a PE or DVT was not increased by abstaining from GCS. After PM two groups of 11 312 patients each, one with and one without GCS application, were formed. When comparing the two groups, the relative risk (RR) for the occurrence of a pulmonary embolism was: Low Risk 0.99 [CI95% 0.998–1.000]; Moderate Risk 0.999 [CI95% 0.95–1.003]; High Risk 0.996 [CI95% 0.992–1.000] (p > 0.05). The incidence of PE in the total group LMWH alone was 0.1% (n = 16). In the total group using LMWH + GCS, the incidence was 0.3% (n = 29). RR after PM was 0.999 [CI95% 0.998–1.00].
Conclusion: In comparison to prior studies with only small numbers of patients our trial shows in a large group of patients with moderate and high risk developing VTE we can support the view that abstaining from GCS-use does not increase the incidence of symptomatic or fatal PE and symptomatic DVT.