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Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples by John Jerald Villamanca; Lemuel John Hermogino; Katherine Denise Ong; Brian Paguia; Lorenzo Abanilla; Antonio Lim; Lara Mae Angeles; Bernadette Espiritu; Maura Isais; Rock Christian Tomas; Pia Marie Albano is a Chemistry article available to read on EtoBox.

What is Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples about?

The early and accurate detection of colorectal cancer (CRC) significantly affects its prognosis and clinical management. However, current standard diagnostic procedures for CRC often lack sensitivity and specificity since most rely on visual examination. Hence, there is a need to develop more accurate methods for its diagnosis. Support vector machine (SVM) and feedforward neural network (FNN) models were designed using the Fourier transform infrared (FT-IR) spectral data of several colorectal tissues that were unanimously identified as either benign or malignant by different unrelated pathologists. The set of samples in which the pathologists had discordant readings were then analyzed using the AI models described above. Between the SVM and NN models, the NN model was able to outperform the SVM model based on their prediction confidence scores. Using the spectral data of the concordant samples as training set, the FNN was able to predict the histologically diagnosed malignant tissues ( = 118) at 59.9-99.9% confidence (average = 93.5%). Of the 118 samples, 84 (71.18%) were classified with an above average confidence score, 34 (28.81%) classified below the average confidence score, a

Who reads Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples?

Author

John Jerald Villamanca; Lemuel John Hermogino; Katherine Denise Ong; Brian Paguia; Lorenzo Abanilla; Antonio Lim; Lara Mae Angeles; Bernadette Espiritu; Maura Isais; Rock Christian Tomas; Pia Marie Albano

Published

2022

Language

EN

Field

Chemistry (Physical Sciences)