ABSTRACT
Background: The attainment of precise posterior occlusion alignment necessitates a deeper understanding of the clinical efficacy of aligner therapy. This study aims to determine whether the treatment goals defined in the virtual planning of aligner therapy are effectively implemented in clinical practice, with a particular focus on the influence of distalization distances on potential vertical side effects. Methods: In this retrospective, non-interventional investigation, a cohort of 20 individuals undergoing Invisalign® treatment was examined. Pre- and post-treatment maxillary clinical and ClinCheck® casts were superimposed utilizing a surface-surface matching algorithm on palatal folds, median palatine raphe, and unmoved teeth as the stable references. The effectivity of planned versus clinical movements was evaluated. Groupings were based on distalization distances, planned vertical movements, and Class II elastic prescription. Statistics were performed with a two-sample t-test and p-value < 0.05. Results: Clinically achieved distalization was significantly lower than virtually planned distalization, regardless of additional vertical movements, where a lack of implementation was contingent upon the extent of distalization, with no mitigating effects observed with the application of Class II elastics. Intriguingly, no adverse vertical side effects were noted; however, the intended intrusions or extrusions, as per the therapeutic plans, remained unattainable regardless of the magnitude of distalization. Conclusions: These findings underscore the imperative for future investigations to delve deeper into the intricacies surrounding translational mesio-distal and vertical movements, thereby enhancing predictability within orthodontic practice. To facilitate successful clinical implementation of vertical and translational movements via aligners, the incorporation of sliders emerges as a promising strategy for bolstering anchorage reinforcement.
ABSTRACT
OBJECTIVES: To investigate the potential and limitations of utilizing transformer-based report annotation for on-site development of image-based diagnostic decision support systems (DDSS). METHODS: The study included 88,353 chest X-rays from 19,581 intensive care unit (ICU) patients. To label the presence of six typical findings in 17,041 images, the corresponding free-text reports of the attending radiologists were assessed by medical research assistants ("gold labels"). Automatically generated "silver" labels were extracted for all reports by transformer models trained on gold labels. To investigate the benefit of such silver labels, the image-based models were trained using three approaches: with gold labels only (MG), with silver labels first, then with gold labels (MS/G), and with silver and gold labels together (MS+G). To investigate the influence of invested annotation effort, the experiments were repeated with different numbers (N) of gold-annotated reports for training the transformer and image-based models and tested on 2099 gold-annotated images. Significant differences in macro-averaged area under the receiver operating characteristic curve (AUC) were assessed by non-overlapping 95% confidence intervals. RESULTS: Utilizing transformer-based silver labels showed significantly higher macro-averaged AUC than training solely with gold labels (N = 1000: MG 67.8 [66.0-69.6], MS/G 77.9 [76.2-79.6]; N = 14,580: MG 74.5 [72.8-76.2], MS/G 80.9 [79.4-82.4]). Training with silver and gold labels together was beneficial using only 500 gold labels (MS+G 76.4 [74.7-78.0], MS/G 75.3 [73.5-77.0]). CONCLUSIONS: Transformer-based annotation has potential for unlocking free-text report databases for the development of image-based DDSS. However, on-site development of image-based DDSS could benefit from more sophisticated annotation pipelines including further information than a single radiological report. CLINICAL RELEVANCE STATEMENT: Leveraging clinical databases for on-site development of artificial intelligence (AI)-based diagnostic decision support systems by text-based transformers could promote the application of AI in clinical practice by circumventing highly regulated data exchanges with third parties. KEY POINTS: ⢠The amount of data from a database that can be used to develop AI-assisted diagnostic decision systems is often limited by the need for time-consuming identification of pathologies by radiologists. ⢠The transformer-based structuring of free-text radiological reports shows potential to unlock corresponding image databases for on-site development of image-based diagnostic decision support systems. ⢠However, the quality of image annotations generated solely on the content of a single radiology report may be limited by potential inaccuracies and incompleteness of this report.
ABSTRACT
Although CT and MRI are standard procedures in cirrhosis diagnosis, differentiation of etiology based on imaging is not established. This proof-of-concept study explores the potential of deep learning (DL) to support imaging-based differentiation of the etiology of liver cirrhosis. This retrospective, monocentric study included 465 patients with confirmed diagnosis of (a) alcoholic (n = 221) and (b) other-than-alcoholic (n = 244) cirrhosis. Standard T2-weighted single-slice images at the caudate lobe level were randomly split for training with fivefold cross-validation (85%) and testing (15%), balanced for (a) and (b). After automated upstream liver segmentation, two different ImageNet pre-trained convolutional neural network (CNN) architectures (ResNet50, DenseNet121) were evaluated for classification of alcohol-related versus non-alcohol-related cirrhosis. The highest classification performance on test data was observed for ResNet50 with unfrozen pre-trained parameters, yielding an area under the receiver operating characteristic curve of 0.82 (95% confidence interval (CI) 0.71-0.91) and an accuracy of 0.75 (95% CI 0.64-0.85). An ensemble of both models did not lead to significant improvement in classification performance. This proof-of-principle study shows that deep-learning classifiers have the potential to aid in discriminating liver cirrhosis etiology based on standard MRI.
