An ANN model for the differential diagnosis of tuberculosis and sarcoidosis

A Review of Machine Learning Algorithms for Biomedical Applications

As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.

Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques

Sarcoidosis is frequently misdiagnosed as tuberculosis (TB) and consequently mistreated due to inherent limitations in radiological presentations. Clinically, to distinguish sarcoidosis from TB, physicians usually employ biopsy tissue diagnosis and blood tests; this approach is painful for patients, time-consuming, expensive, and relies on techniques prone to human error. This study proposes a computer-aided diagnosis method to address these issues. This method examines seven EfficientNet designs that were fine-tuned and compared for their abilities to categorize X-ray images into three categories: normal, TB-infected, and sarcoidosis-infected. Furthermore, the effects of stain normalization on performance were investigated using Reinhard’s and Macenko’s conventional stain normalization procedures. This procedure aids in improving diagnostic efficiency and accuracy while cutting diagnostic costs. A database of 231 sarcoidosis-infected, 563 TB-infected, and 1010 normal chest X-ray images was created using public databases and information from several national hospitals. The EfficientNet-B4 model attained accuracy, sensitivity, and precision rates of 98.56%, 98.36%, and 98.67%, respectively, when the training X-ray images were normalized by the Reinhard stain approach, and 97.21%, 96.9%, and 97.11%, respectively, when normalized by Macenko’s approach. Results demonstrate that Reinhard stain normalization can improve the performance of EfficientNet -B4 X-ray image classification. The proposed framework for identifying pulmonary sarcoidosis may prove valuable in clinical use.

Development of a random forest model to classify sarcoidosis and tuberculosis

OBJECTIVE

To identify significant diagnostic factors and establish a predictive model for diagnosis of sarcoidosis and tuberculosis.

METHODS

This study included 252 patients (123 cases of lung sarcoidosis and 129 cases of lung tuberculosis) who underwent laboratory evaluation, including routine hematologic testing, serum immunology, blood coagulation, angiotensin-converting enzyme, and T lymphocyte subset. The factors that statistically different between the two groups were identified by an independent sample t test first, and then processed by the random forest model to distinguish two diseases with the classification function. Moreover, the diagnostic performance of the predictive random forest model was evaluated through the identification of individual contribution of various diagnostic factors conducted by the model.

RESULTS

The random forest model revealed a classification error rate of 24.9%. Among all of the statistically significant diagnostic factors, the individual factors with the greatest and second contribution were angiotensin-converting enzyme and prothrombin time, respectively. The area under the receiver operating characteristic (ROC) curve of the random forest prediction model was 0.915.

CONCLUSION

The random forest model can be used to distinguish between sarcoidosis and tuberculosis by incorporating statistically significant diagnostic factors, which is of potential clinical application value.