Culture and Community Interactions: Essentials for understanding in a globalised world

"Introduction to the issue

The impact of computed high b-value images on the diagnostic accuracy of DWI for prostate cancer: A receiver operating characteristics analysis.

To evaluate the performance of computed high b value diffusion-weighted images (DWI) in prostate cancer detection. 97 consecutive patients who had undergone multiparametric MRI of the prostate followed by biopsy were reviewed. Five radiologists independently scored 138 lesions on native high b-value images (b = 1200 s/mm2), apparent diffusion coefficient (ADC) maps, and computed high b-value images (contrast equivalent to b = 2000 s/mm2) to compare their diagnostic accuracy. Receiver operating characteristic (ROC) analysis and McNemar's test were performed to assess the relative performance of computed high b value DWI, native high b-value DWI and ADC maps. No significant difference existed in the area under the curve (AUC) for ROCs comparing B1200 (b = 1200 s/mm2) to computed B2000 (c-B2000) in 5 readers. In 4 of 5 readers c-B2000 had significantly increased sensitivity and/or decreased specificity compared to B1200 (McNemar's p < 0.05), at selected thresholds of interpretation. ADC maps were less accurate than B1200 or c-B2000 for 2 of 5 readers (P < 0.05). This study detected no consistent improvement in overall diagnostic accuracy using c-B2000, compared with B1200 images. Readers detected more cancer with c-B2000 images (increased sensitivity) but also more false positive findings (decreased specificity)

Adverse Local Tissue Reaction due to Acetabular Corrosion in Modular Dual-Mobility Constructs

Dual-mobility (DM) bearings in total hip arthroplasty (THA) have been reported to reduce dislocation rates, especially in high-risk patients, and are being rapidly adopted in primary and revision THAs. However, this technology introduces additional interfaces that have the potential to result in unforeseen complications. We present a series of 3 patients with mechanically assisted crevice corrosion at the acetabular component–metal dual-mobility liner interface. Consequently, we urge judicious use and close clinical observation of this new, effective technology in THA

Early failure of sequentially annealed polyethylene in total knee arthroplasty

Improvements in the processing of polyethylene have led to a dramatic reduction in wear rates in total hip arthroplasty. This led to the adoption of modern highly cross-linked polyethylene in total knee arthroplasty (TKA). However, the differences in modes of wear and failure between total hip arthroplasty and TKA have tempered expectations regarding similar decreases in polyethylene-related complications in TKA. We present a case of early catastrophic failure of a modern sequentially irradiated and annealed highly cross-linked polyethylene insert only 5 years after contemporary cementless TKA

Fibered Confocal Microscopy of Bladder Tumors: An ex Vivo Study

Background and Purpose: The inadequacy of white-light cystoscopy to detect flat bladder tumors is well recognized. Great interest exists in developing other imaging technologies to augment or supplant conventional cystoscopy. Fibered confocal microscopy offers the promise of providing in vivo histopathologic information to help distinguish malignant from benign bladder lesions. We report the initial use of this technology to visualize tumors in the human bladder. Materials and Methods: We performed ex vivo fibered confocal imaging of fresh radical cystectomy specimens using the Mauna Kea Technologies Cellvizio system. The findings were compared with results from standard histopathology. Results: The bladders of four patients were imaged using the fibered confocal microscope. Normal and neoplastic urothelium manifested differences in cellular and vascular density. Conclusion: This study demonstrates the feasibility of using fibered confocal microscopy to detect histologic differences between normal and neoplastic urothelium, and establishes a foundation for the use of fiber-based confocal microscopy in clinical studies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78112/1/end.2008.0524.pd

Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments

Image quality assessment for machine learning tasks using meta-reinforcement learning

In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the performance of the task predictor provides an objective estimate of task amenability. In this work, we use an IQA controller to predict the task amenability which, itself being parameterised by neural networks, can be trained simultaneously with the task predictor. We further develop a meta-reinforcement learning framework to improve the adaptability for both IQA controllers and task predictors, such that they can be fine-tuned efficiently on new datasets or meta-tasks. We demonstrate the efficacy of the proposed task-specific, adaptable IQA approach, using two clinical applications for ultrasound-guided prostate intervention and pneumonia detection on X-ray images