Acute Leukemia Diagnosis Through AI-Enhanced Attenuated Total Reflection Fourier Transform Infrared Spectroscopy of Peripheral Blood Smears.

Acute leukemia, a highly perilous cancer, is diagnosed using invasive procedures like bone marrow aspirate and biopsy (BMA/BMB). This study investigated the use of artificial intelligence (AI)-enhanced Fourier transform infrared (FT-IR) spectroscopy as a non-invasive, reagent-free diagnostic alternative with high sensitivity and specificity. The spectral peak patterns of peripheral blood smears (PBS) from clinically healthy individuals ( = 50) BMA/BMB-confirmed acute leukemia patients ( = 50) were examined in the 1800-850 cm range.

Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy.

Given the increasing prevalence of lung cancer worldwide, an auxiliary diagnostic method is needed alongside the microscopic examination of biopsy samples, which is dependent on the skills and experience of pathologists. Thus, this study aimed to advance lung cancer diagnosis by developing five (5) artificial neural network (NN) models that can discriminate malignant from benign samples based on infrared spectral data of lung tumors (n = 122; 56 malignant, 66 benign). NNs were benchmarked with classical machine learning (CML) models.

Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks.

In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification.

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues.

The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (H&E)-stained biopsies-is prone to bias since it greatly relies on visual examination. Hence, there is a need to develop a more sensitive and specific method for diagnosing cancer. Here, Fourier transform infrared (FTIR) spectroscopy of thyroid tumors (n = 164; 76 malignant, 88 benign) was performed and five (5) neural network (NN) models were designed to discriminate the obtained spectral data.

ATR-FTIR spectroscopy as adjunct method to the microscopic examination of hematoxylin and eosin-stained tissues in diagnosing lung cancer.

Lung cancer remains the leading cause of cancer-related death worldwide. Since prognosis and treatment outcomes rely on fast and accurate diagnosis, there is a need for more cost-effective, sensitive, and specific method for lung cancer detection. Thus, this study aimed to determine the ability of ATR-FTIR in discriminating malignant from benign lung tissues and evaluate its concordance with H&E staining.