Urinary and Sexual Impact of Robotic Radical Prostatectomy: Reporting of Patient-reported Outcome Measures in the First Year after Radical Prostatectomy in a Contemporary Multicentre Cohort in the United Kingdom.

Background and objective: Radical prostatectomy (RP) is an established treatment for localised prostate cancer that can have a significant impact on urinary and sexual function, with recovery over time. Our aim was to describe functional recovery in the first year after RP, reporting descriptive outcomes alongside validated patient-reported outcome measure scores (Expanded Prostate Cancer Index Composite, EPIC-26). Methods: Men undergoing RP between September 2015 and November 2019 completed EPIC-26 at baseline and 1, 3, 6, and 12 mo. Key findings and limitations: Overall, 2030 men consented to participation, underwent RP, and completed EPIC-26. At baseline, 97% were pad-free (1928/1996; 95% confidence interval [CI] 96-97%) and 77% were leak-free and pad-free (1529/1996; 95% CI 75-78), with a median EPIC-26 incontinence domain score of 100 (interquartile range [IQR] 86-100). At 12 mo, 65% were pad-free (904/1388; 95% CI 63-68%) and 42% were leak-free and pad-free (583/1388; 95% CI 39-45%), with a median EPIC-26 score of 76 (IQR 61-100). While one in three men reported wearing a pad at 12 mo, fewer than one in ten men needed more than 1 pad/d. At baseline, 1.9% reported a "moderate or big problem" with urine leakage, which increased to 9.7% at 12 mo. At baseline, the median sexual domain score among 1880 men was 74 (IQR 43-92) and 52% had erections sufficient for intercourse without medication (975/1880; 95% CI 50-54%). Among these 975 men, 630 responded at 12 mo, of whom 17% reported sufficient erections for intercourse (105/630; 95% CI 14-20%), without medication in 6% (37/630; 95% CI 4-8%) and needing medication in 11% (68/630; 95% CI 9-13%); the median EPIC-26 domain score was 26 (IQR 13-57). Conclusions and clinical implications: Reporting of functional outcomes after RP in terms of easily understood concepts such as pad-free and leak-free status, and erections with and with medication, alongside the classical report using EPIC-26 domain scores, increases the understanding of RP recovery patterns over the first year. Patient summary: At 12 months after surgery for prostate cancer, one in ten men reported a moderate or big problem with urine leakage and one in five men reported sufficient erections.

Development and external validation of a mixed-effects deep learning model to diagnose COVID-19 from CT imaging.

Background: The automatic analysis of medical images has the potential improve diagnostic accuracy while reducing the strain on clinicians. Current methods analyzing 3D-like imaging data, such as computerized tomography imaging, often treat each image slice as individual slices. This may not be able to appropriately model the relationship between slices. Methods: Our proposed method utilizes a mixed-effects model within the deep learning framework to model the relationship between slices. We externally validated this method on a data set taken from a different country and compared our results against other proposed methods. We evaluated the discrimination, calibration, and clinical usefulness of our model using a range of measures. Finally, we carried out a sensitivity analysis to demonstrate our methods robustness to noise and missing data. Results: In the external geographic validation set our model showed excellent performance with an AUROC of 0.930 (95%CI: 0.914, 0.947), with a sensitivity and specificity, PPV, and NPV of 0.778 (0.720, 0.828), 0.882 (0.853, 0.908), 0.744 (0.686, 0.797), and 0.900 (0.872, 0.924) at the 0.5 probability cut-off point. Our model also maintained good calibration in the external validation dataset, while other methods showed poor calibration. Conclusion: Deep learning can reduce stress on healthcare systems by automatically screening CT imaging for COVID-19. Our method showed improved generalizability in external validation compared to previous published methods. However, deep learning models must be robustly assessed using various performance measures and externally validated in each setting. In addition, best practice guidelines for developing and reporting predictive models are vital for the safe adoption of such models.

Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.

Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting millions of people's health and lives in jeopardy. Detecting infected patients early on chest computed tomography (CT) is critical in combating COVID-19. Harnessing uncertainty-aware consensus-assisted multiple instance learning (UC-MIL), we propose to diagnose COVID-19 using a new bilateral adaptive graph-based (BA-GCN) model that can use both 2D and 3D discriminative information in 3D CT volumes with arbitrary number of slices. Given the importance of lung segmentation for this task, we have created the largest manual annotation dataset so far with 7,768 slices from COVID-19 patients, and have used it to train a 2D segmentation model to segment the lungs from individual slices and mask the lungs as the regions of interest for the subsequent analyses. We then used the UC-MIL model to estimate the uncertainty of each prediction and the consensus between multiple predictions on each CT slice to automatically select a fixed number of CT slices with reliable predictions for the subsequent model reasoning. Finally, we adaptively constructed a BA-GCN with vertices from different granularity levels (2D and 3D) to aggregate multi-level features for the final diagnosis with the benefits of the graph convolution network's superiority to tackle cross-granularity relationships. Experimental results on three largest COVID-19 CT datasets demonstrated that our model can produce reliable and accurate COVID-19 predictions using CT volumes with any number of slices, which outperforms existing approaches in terms of learning and generalisation ability. To promote reproducible research, we have made the datasets, including the manual annotations and cleaned CT dataset, as well as the implementation code, available at https://doi.org/10.5281/zenodo.6361963.

Artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings.

Background: Electrocardiogram (ECG) interpretation is an integral part of the clinical ECG workflow; however, this process is often time-consuming and labor-intensive. We aim to develop a rapid, inexpensive means to detect abnormal ECGs using artificial intelligence (AI) from scanned ECG printouts. Methods: The study included 1172 12-lead ECG scans performed in 1172 individuals from a community in Guangzhou, China; 878 (74.9%) were diagnosed with sinus rhythm, and the remaining 294 (25.1%) with abnormal rhythms. A deep learning model consisting of a convolutional neural network based on InceptionV3 and a fully connected layer followed by a GEV activation was trained to classify scanned tracings as either normal or abnormal. Results: In a hold-out testing set, the model achieved a area under curve (AUC), sensitivity, specificity, PPV, and NPV of 0.932 (95% confidence interval [CI]: 0.890, 0.976), 0.816 (95% CI: 0.657, 0.923), 0.993 (95% CI: 0.959, 1.0), 0.969 (95% CI: 0.838, 0.999), and 0.950 (95% CI: 0.90, 0.980) respectively, when using a probability threshold of 0.5. When compared with a physiological expert, these results show comparable performance with a statistically significant increase in specificity and a non-significant decrease in sensitivity at the 95% level. Conclusions: We have developed a rapid, inexpensive, accurate means to detect abnormal ECGs using AI. Easy and accurate identification of such "abnormal" ECGs could allow the mass automated review of ECGs in community settings where abnormal ones could be flagged using AI for detailed clinical review by healthcare professionals.

Murine models of renal ischemia reperfusion injury: An opportunity for refinement using noninvasive monitoring methods.

BACKGROUND: Renal ischemia reperfusion injury (R-IRI) can cause acute kidney injury (AKI) and chronic kidney disease (CKD), resulting in significant morbidity and mortality. To understand the underlying mechanisms, reproducible small-animal models of AKI and CKD are needed. We describe how innovative technologies for measuring kidney function noninvasively in small rodents allow successful refinement of the R-IRI models, and offer the unique opportunity to monitor longitudinally in individual animals the transition from AKI to CKD. METHODS: Male BALB/c mice underwent bilateral renal pedicle clamping (AKI) or unilateral renal pedicle clamping with delayed contralateral nephrectomy (CKD) under isoflurane anesthetic. Transdermal GFR monitoring and multispectral optoacoustic tomography (MSOT) in combination with statistical analysis were used to identify and standardize variables within these models. RESULTS: Pre-clamping anesthetic time was one of the most important predictors of AKI severity after R-IRI. Standardizing pre-clamping time resulted in a more predictably severe AKI model. In the CKD model, MSOT demonstrated initial improvement in renal function, followed by significant progressive reduction in function between weeks 2 and 4. Performing contralateral nephrectomy on day 14 enabled the development of CKD with minimal mortality. CONCLUSIONS: Noninvasive monitoring of global and individual renal function after R-IRI is feasible and reproducible. These techniques can facilitate refinement of kidney injury models and enable the degree of injury seen in preclinical models to be translated to those seen in the clinical setting. Thus, future therapies can be tested in a clinically relevant, noninvasive manner.

Development and validation of a novel prognostic model for predicting AMD progression using longitudinal fundus images.

OBJECTIVE: To develop a prognostic tool to predict the progression of age-related eye disease progression using longitudinal colour fundus imaging. METHODS AND ANALYSIS: Previous prognostic models using deep learning with imaging data require annotation during training or only use a single time point. We propose a novel deep learning method to predict the progression of diseases using longitudinal imaging data with uneven time intervals, which requires no prior feature extraction. Given previous images from a patient, our method aims to predict whether the patient will progress onto the next stage of the disease. The proposed method uses InceptionV3 to produce feature vectors for each image. In order to account for uneven intervals, a novel interval scaling is proposed. Finally, a recurrent neural network is used to prognosticate the disease. We demonstrate our method on a longitudinal dataset of colour fundus images from 4903 eyes with age-related macular degeneration (AMD), taken from the Age-Related Eye Disease Study, to predict progression to late AMD. RESULTS: Our method attains a testing sensitivity of 0.878, a specificity of 0.887 and an area under the receiver operating characteristic of 0.950. We compare our method to previous methods, displaying superior performance in our model. Class activation maps display how the network reaches the final decision. CONCLUSION: The proposed method can be used to predict progression to advanced AMD at some future visit. Using multiple images at different time points improves predictive performance.

Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models.

Fast and accurate diagnosis is essential for the efficient and effective control of the COVID-19 pandemic that is currently disrupting the whole world. Despite the prevalence of the COVID-19 outbreak, relatively few diagnostic images are openly available to develop automatic diagnosis algorithms. Traditional deep learning methods often struggle when data is highly unbalanced with many cases in one class and only a few cases in another; new methods must be developed to overcome this challenge. We propose a novel activation function based on the generalized extreme value (GEV) distribution from extreme value theory, which improves performance over the traditional sigmoid activation function when one class significantly outweighs the other. We demonstrate the proposed activation function on a publicly available dataset and externally validate on a dataset consisting of 1,909 healthy chest X-rays and 84 COVID-19 X-rays. The proposed method achieves an improved area under the receiver operating characteristic (DeLong's p-value < 0.05) compared to the sigmoid activation. Our method is also demonstrated on a dataset of healthy and pneumonia vs. COVID-19 X-rays and a set of computerized tomography images, achieving improved sensitivity. The proposed GEV activation function significantly improves upon the previously used sigmoid activation for binary classification. This new paradigm is expected to play a significant role in the fight against COVID-19 and other diseases, with relatively few training cases available.

A systematic review of methodology used in the development of prediction models for future asthma exacerbation.

BACKGROUND: Clinical prediction models are widely used to guide medical advice and therapeutic interventions. Asthma is one of the most common chronic diseases globally and is characterised by acute deteriorations. These exacerbations are largely preventable, so there is interest in using clinical prediction models in this area. The objective of this review was to identify studies which have developed such models, determine whether consistent and appropriate methodology was used and whether statistically reliable prognostic models exist. METHODS: We searched online databases MEDLINE (1948 onwards), CINAHL Plus (1937 onwards), The Cochrane Library, Web of Science (1898 onwards) and ClinicalTrials.gov, using index terms relating to asthma and prognosis. Data was extracted and assessment of quality was based on GRADE and an early version of PROBAST (Prediction study Risk of Bias Assessment Tool). A meta-analysis of the discrimination and calibration measures was carried out to determine overall performance across models. RESULTS: Ten unique prognostic models were identified. GRADE identified moderate risk of bias in two of the studies, but more detailed quality assessment via PROBAST highlighted that most models were developed using highly selected and small datasets, incompletely recorded predictors and outcomes, and incomplete methodology. None of the identified models modelled recurrent exacerbations, instead favouring either presence/absence of an event, or time to first or specified event. Preferred methodologies were logistic regression and Cox proportional hazards regression. The overall pooled c-statistic was 0.77 (95% confidence interval 0.73 to 0.80), though individually some models performed no better than chance. The meta-analysis had an I2 value of 99.75% indicating a high amount of heterogeneity between studies. The majority of studies were small and did not include internal or external validation, therefore the individual performance measures are likely to be optimistic. CONCLUSIONS: Current prognostic models for asthma exacerbations are heterogeneous in methodology, but reported c-statistics suggest a clinically useful model could be created. Studies were consistent in lacking robust validation and in not modelling serial events. Further research is required with respect to incorporating recurrent events, and to externally validate tools in large representative populations to demonstrate the generalizability of published results.