Intelligence-based medicine最新文献

Coronavirus disease 2019 (COVID-19) has become a pandemic all over the world and has spread rapidly. To distinguish between infected and non-infected areas, there is a critical need for segmentation methods that can identify infected areas from Chest Computed Tomography (CT) scans. In recent years, deep learning has become the most widely used approach for medical image segmentation, including the identification of infected areas in Chest CT scans. We propose an encoder-decoder based on the U-NET architecture for segmenting the MedSeg dataset, which contains lung CT scans. To study the effect of input dimensions on the model's output results, we gave CT images with dimensions of 224 × 224, 256 × 256, and 512 × 512 as inputs to the model. The results showed that 224 × 224 achieved higher results compared to 256 × 256 and 512 × 512, with a $dic{e}_{coef}$ of 81.36, accuracy of 87.65, sensitivity of 84.71, and specificity of 88.35. Additionally, the 224 × 224 input based on the proposed model achieved the highest number of correct answers compared to previous U-net methods. The proposed model can be applied as an effective screening tool to help primary service staff better refer suspected patients to specialists.

Diagnosing lung cancer is difficult due to the complexity of the nature of nodules. CT scan imaging is the most common imaging to diagnosis of lung cancer. Detection of nodules from these images is a challenge for radiologists and doctors. In recent years, neural networks have been developed for automatic, faster and more accurate diagnosis of diseases from medical images. In the present study, a new improved U-Net neural network is introduced for the automatic detection and segmentation of pulmonary nodules. The evaluation of this model has been done on LIDC-IDRI database. Our results have high values of recall, specificity and accuracy. The highest Recall value is 97.97 and is related to Juxtra-vascular. Specificity and accuracy for non-solid, partially solid and tiny has a value of 96.99.

Introduction

Methods

Results

Remarks and significance

Introduction

Methods

Conclusion

Emergency department (ED) overcrowding is a significant problem in the US. This paper develops a decision model to mitigate ED overcrowding by helping hospitals proactively plan patient boarding processes. The information obtained after the initial assessment of COVID-19 patients in the ED, including patient demographics and medical history, is utilized to predict ICU admission earlier. The predicted information can be communicated with the inpatient unit to prepare an ICU bed for the patients who need ICU care. As a result, the boarding time when patients wait for an ICU bed to be ready can be reduced. The data used in this study included 100 features and 19,155 COVID-19 patients from an academic medical center located in the Southeast United States. Multiple feature selection methods along with Extreme Gradient Boosting (XGBoost) were utilized to develop the models. The parameters of the XGBoost models are optimized using simulated annealing (SA). Among the proposed models, the best model included ten features and resulted in an area under the curve (AUC) of 89.2%, which is the highest among the models proposed in the literature. The proposed prediction model allows hospital administrators to allocate ICU beds more efficiently, enhance patient flow, and mitigate ED overcrowding.

Background

Polycystic Ovary Syndrome (PCOS) is reported to affect between 4% and 21% of reproductive aged people with ovaries. It is a heterogeneous condition with a lack of established phenotypes that address the range of reproductive and metabolic features present in PCOS. These reproductive and metabolic features may result in patients undergoing a variety of relevant laboratory tests. Previous work has led to the gathering of laboratory test results from a PCOS specific forum, hosted on a website called reddit.

Objectives

In this paper, laboratory results and body mass index (BMI) posted on the PCOS reddit forum were clustered to show the usefulness of the PCOS forum for PCOS research and validate existing PCOS phenotypes or discover other appropriate phenotypes.

Methods and results

Over 1500 sets of PCOS-related reddit laboratory test results and BMIs were clustered using nearest neighbour imputation and K-means clustering. However, only non-imputed data was included in the final clusters. Kernel Density Estimation plots were used to display the distinct clusters. The clustered test results suggested the existence of distinct metabolic and reproductive phenotypes, as well as a group displaying mild features of both types of dysregulations and a group skewed towards normal results. It was also possible to separate the groups further into distinct hypothyroid groups within the mixed dysregulation group and to separate insulin resistant and diabetes-like groups within the metabolic group.

Conclusions

This research further validates the usefulness of exploring alternate data sources in the age of the internet and machine learning. The reddit clusters reinforced the existing notion that people with PCOS can be separated into a primarily metabolic pathology group, a primarily reproductive pathology group and an in between group with pathology in both domains.

Introduction

Deformable image registration (DIR) plays a vital role in adaptive radiotherapy (ART). For the clinical implementation of DIR, evaluation of deformation accuracy is a critical step. While contour-based metrics, for example Dice similarity coefficient (DSC), are widely implemented for DIR validation, they require delineation of contours which is time-consuming and would cause hold-ups in an ART workflow. Therefore, this work aims to accomplish the prediction of DSC using various metrics based on deformation vector field (DVF) by applying machine learning (ML), in order to provide an efficient means of DIR validation with minimised human intervention.

Methods

Planning CT image was deformed to the cone-beam CT images for 20 prostate cancer patients. Various DVF-based metrics and DSC were calculated, and the former was used as input features to predict the latter using three ML models, namely linear regression (LR), Nu Support Vector Regression (NuSVR) and Random Forest Regressor (RFR). Four datasets were used for analysis: 1) prostate, 2) bladder, 3) rectum and 4) all the organs combined. Average mean absolute error (MAE) was computed to evaluate the model performance. The classification performance of the best-performing model was further evaluated, and the prediction interval and feature importance were calculated.

Results

Overall, RFR achieved the lowest average MAE, ranging between 0.045 and 0.069 for the four datasets, while LR and NuSVR had slightly poorer performances. Analysis on the results of best-performing model showed that sensitivity and specificity of 0.86 and 0.51, respectively, were obtained when a prediction threshold of 0.85 was used to classify the fourth dataset. Jacobian determinant was found to be a significant contributor to the predictions of all four datasets using this model.

Conclusion

This study demonstrated the potential of several ML models, especially RFR, to be applied for prediction of DSC to speed up the DIR validation process.

Introduction

Stroke remains the second leading cause of death worldwide, with many survivors facing significant disabilities. In acute stroke care, the timeless adage 'Time is brain' underscores the vital need for quick action. Innovative Artificial Intelligence (AI) technology potentially enables swift detection and management of acute ischemic strokes, revolutionizing acute stroke care towards enhanced automation.

Methods

The study is registered with Prospero under CRD42024496716 and adheres to the Problem, Intervention, Comparison, and Outcomes framework (PICO). The analysis used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, Cochrane database, IEEE, Web of Science, ArXiv, MedRxiv, and Semantic Scholar. The articles included were published between 2019 and 2023. Out of 1528 articles identified, thirty-seven met the inclusion criteria.

Results

We compared AI-augmented Large Vessel Occlusion (LVO) detection and non-AI LVO detection in various patient processing times related to emergent endovascular therapy in acute ischemic strokes. Triage Time, Door-to-Intervention Notification Time (INR), and Door-to -Arterial Puncture Time revealed an odds ratio (OR) of 0.39 (95 % CI: 0.29–0.54, p < 0.001), 0.30 (95 % CI: 0.21–0.42, p < 0.001), and 0.50 (95 % CI: 0detection 0.30–0.82, p = 0.007), respectively -- all of which had negligible heterogeneity (I^2 = 0). CT-to-Puncture-Time and Door-to-CTA-Time yielded an OR of 0.57 (95 % CI: 0.31–1.04, p = 0.065) and 0.77 (95 % CI: 0.37–1.60, p = 0.489), respectively -- both of which had negligible heterogeneity (I^2 = 0). The Last Known Well (LWK) to Time of Arrival resulted in an OR of 1.15 (95 % CI: 0.83–1.59, p = 0.409, I^2 = 0). AI stroke detection sensitivity OR of 0.91 (95 % CI: 0.88–0.95, p < 0.001) should be interpreted with potential heterogeneity in mind (I^2 = 69.3). National Institute of Health score (NIHSS) mean of 16.20 (95 % CI: 14.96–17.45, p = 0.001, I^2 = 0). Patient Transfer-Times between primary and comprehensive stroke centers generated an OR of 0.98 (95 % CI: 0.73–1.32, p = 893, I^2 = 0). Similarly, Door-in-Door-Out Time (DIDO) had an OR of 1.19 (95 % CI: 0.21–6.88, p = 0.848) and low heterogeneity (I^2 = 5.1). The results indicated significant differences across several parameters between the AI augmentation and non-AI groups.

Conclusion

Our findings highlight how AI augments healthcare providers' ability to detect and manage strokes swiftly and accurately within acute care settings. As these technologies progress, healthcare organizations mature, and AI becomes more integrated into healthcare systems, longitudinal studies are critical in evaluating its impact on workflow efficiency, cost-effectiveness, and clinical outcomes.