External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival

Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units < 800

The correct diagnosis of uric acid (UA) stones has important clinical implications since patients with a high risk of perioperative morbidity may be spared surgical intervention and be offered alkalization therapy. We developed and validated a machine learning (ML)-based model to identify stones on computed tomography (CT) images and simultaneously classify UA stones from non-UA stones. An international, multicenter study was performed on 202 patients who received percutaneous nephrolithotomy for kidney stones with HU < 800. Data from 156 (77.2%) patients were used for model development, while data from 46 (22.8%) patients from a multinational institution were used for external validation. A total of 21,074 kidney and stone contour-annotated CT images were trained with the ResNet-18 Mask R-convolutional neural network algorithm. Finally, this model was concatenated with demographic and clinical data as a fully connected layer for stone classification. Our model was 100% sensitive in detecting kidney stones in each patient, and the delineation of kidney and stone contours was precise within clinically acceptable ranges. The development model provided an accuracy of 99.9%, with 100.0% sensitivity and 98.9% specificity, in distinguishing UA from non-UA stones. On external validation, the model performed with an accuracy of 97.1%, with 89.4% sensitivity and 98.6% specificity. SHAP plots revealed stone density, diabetes mellitus, and urinary pH as the most important features for classification. Our ML-based model accurately identified and delineated kidney stones and classified UA stones from non-UA stones with the highest predictive accuracy reported to date. Our model can be reliably used to select candidates for an earlier-directed alkalization therapy.

Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials

Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.

Multilayer perceptron-based prediction of stroke mimics in prehospital triage

The identification of stroke mimics (SMs) in patients with stroke could lead to delayed diagnosis and waste of medical resources. Multilayer perceptron (MLP) was proved to be an accurate tool for clinical applications. However, MLP haven't been applied in patients with suspected stroke onset within 24 h. Here, we aimed to develop a MLP model to predict SM in patients. We retrospectively reviewed the data of patients with a prehospital diagnosis of suspected stroke between July 2017 and June 2021. SMs were confirmed during hospitalization. We included demographic information, clinical manifestations, medical history, and systolic and diastolic pressure on admission. First, the cohort was randomly divided into a training set (70%) and an external testing set (30%). Then, the least absolute shrinkage and selection operator (LASSO) method was used in feature selection and an MLP model was trained based on the selected items. Then, we evaluated the performance of the model using the ten-fold cross validation method. Finally, we used the external testing set to compare the MLP model with FABS scoring system (FABS) and TeleStroke Mimic Score (TM-Score) using a receiver operator characteristic (ROC) curve. In total, 402 patients were included. Of these, 82 (20.5%) were classified as SMs. During the ten-fold cross validation, the mean area under the ROC curve (AUC) of 10 training sets and 10 validation sets were 0.92 and 0.87, respectively. In the external testing set, the AUC of the MLP model was significantly higher than that of the FABS (0.855 vs. 0.715, P = 0.038) and TM-Score (0.855 vs. 0.646, P = 0.006). The MLP model had significantly better performance in predicting SMs than FABS and TM-Score.