Region of Interest Selection for Functional Features

Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform

Background: The sensitivity and specificity of current breath biomarkers are often inadequate for effective cancer screening, particularly in colorectal cancer (CRC). While a few exhaled biomarkers in CRC exhibit high specificity, they lack the requisite sensitivity for early-stage detection, thereby limiting improvements in patient survival rates. Methods: In this study, we developed an advanced Mass Spectrometry-based volatilomics platform, complemented by an enhanced breath sampler. The platform integrates artificial intelligence (AI)-assisted algorithms to detect multiple volatile organic compounds (VOCs) biomarkers in human breath. Subsequently, we applied this platform to analyze 364 clinical CRC and normal exhaled samples. Results: The diagnostic signatures, including 2-methyl, octane, and butyric acid, generated by the platform effectively discriminated CRC patients from normal controls with high sensitivity (89.7%), specificity (86.8%), and accuracy (AUC = 0.91). Furthermore, the metastatic signature correctly identified over 50% of metastatic patients who tested negative for carcinoembryonic antigen (CEA). Fecal validation indicated that elevated breath biomarkers correlated with an inflammatory response guided by Bacteroides fragilis in CRC. Conclusion: This study introduces a sophisticated AI-aided Mass Spectrometry-based platform capable of identifying novel and feasible breath biomarkers for early-stage CRC detection. The promising results position the platform as an efficient noninvasive screening test for clinical applications, offering potential advancements in early detection and improved survival rates for CRC patients.

Harnessing Machine Learning for Prediction of Postoperative Pulmonary Complications: Retrospective Cohort Design

Postoperative pulmonary complications (PPCs) are significant causes of postoperative morbidity and mortality. This study presents the utilization of machine learning for predicting PPCs and aims to identify the important features of the prediction models. This study used a retrospective cohort design and collected data from two hospitals. The dataset included perioperative variables such as patient characteristics, preexisting diseases, and intraoperative factors. Various algorithms, including logistic regression, random forest, light-gradient boosting machines, extreme-gradient boosting machines, and multilayer perceptrons, have been employed for model development and evaluation. This study enrolled 111,212 adult patients, with an overall incidence rate of 8.6% for developing PPCs. The area under the receiver-operating characteristic curve (AUROC) of the models was 0.699-0.767, and the f1 score was 0.446-0.526. In the prediction models, except for multilayer perceptron, the 10 most important features were obtained. In feature-reduced models, including 10 important features, the AUROC was 0.627-0.749, and the f1 score was 0.365-0.485. The number of packed red cells, urine, and rocuronium doses were similar in the three models. In conclusion, machine learning provides valuable insights into PPC prediction, significant features for prediction, and the feasibility of models that reduce the number of features.

Machine learning-based obesity classification considering 3D body scanner measurements

Obesity can cause various diseases and is a serious health concern. BMI, which is currently the popular measure for judging obesity, does not accurately classify obesity; it reflects the height and weight but ignores the characteristics of an individual's body type. In order to overcome the limitations of classifying obesity using BMI, we considered 3-dimensional (3D) measurements of the human body. The scope of our study was limited to Korean subjects. In order to expand 3D body scan data clinically, 3D body scans, Dual-energy X-ray absorptiometry, and Bioelectrical Impedance Analysis data was collected pairwise for 160 Korean subjects. A machine learning-based obesity classification framework using 3D body scan data was designed, validated through Accuracy, Recall, Precision, and F1 score, and compared with BMI and BIA. In a test dataset of 40 people, BMI had the following values: Accuracy: 0.529, Recall: 0.472, Precision: 0.458, and F1 score: 0.462, while BIA had the following values: Accuracy: 0.752, Recall: 0.742, Precision: 0.751, and F1 score: 0.739. Our proposed model had the following values: Accuracy: 0.800, Recall: 0.767, Precision: 0.842, and F1 score: 0.792. Thus, our accuracy was higher than BMI as well as BIA. Our model can be used for obesity management through 3D body scans.

S2FLNet: Hepatic steatosis detection network with body shape

Fat accumulation in the liver cells can increase the risk of cardiac complications and cardiovascular disease mortality. Therefore, a way to quickly and accurately detect hepatic steatosis is critically important. However, current methods, e.g., liver biopsy, magnetic resonance imaging, and computerized tomography scan, are subject to high cost and/or medical complications. In this paper, we propose a deep neural network to estimate the degree of hepatic steatosis (low, mid, high) using only body shapes. The proposed network adopts dilated residual network blocks to extract refined features of input body shape maps by expanding the receptive field. Furthermore, to classify the degree of steatosis more accurately, we create a hybrid of the center loss and cross entropy loss to compact intra-class variations and separate inter-class differences. We performed extensive tests on the public medical dataset with various network parameters. Our experimental results show that the proposed network achieves a total accuracy of over 82% and offers an accurate and accessible assessment for hepatic steatosis.

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.

Decoding different working memory states during an operation span task from prefrontal fNIRS signals

We propose an effective and practical decoding method of different mental states for potential applications for the design of brain-computer interfaces, prediction of cognitive behaviour, and investigation of cognitive mechanism. Functional near infrared spectroscopy (fNIRS) signals that interrogated the prefrontal and parietal cortices and were evaluated by generalized linear model were recorded when nineteen healthy adults performed the operation span (OSPAN) task. The oxygenated hemoglobin changes during OSPAN, response, and rest periods were classified with a support vector machine (SVM). The relevance vector regression algorithm was utilized for prediction of cognitive performance based on multidomain features of fNIRS signals from the OSPAN task. We acquired decent classification accuracies for OSPAN vs. response (above 91.2%) and for OSPAN vs. rest (above 94.7%). Eight of the ten cognitive testing scores could be predicted from the combination of OSPAN and response features, which indicated the brain hemodynamic responses contain meaningful information suitable for predicting cognitive performance.