Building and Interpreting Artificial Neural Network Models for Biological Systems

Nair, T. Murlidharan

doi:10.1007/978-1-0716-0826-5_8

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…The comprehensive analysis of the diagnostic efficacy of each metabolic parameter and DMC in this study showed that a set of stdSUVavg data had a good diagnostic efficacy, and the sensitivity and specificity of stdSUVavg at 1 h and stdSUVavg at 4 h were relatively high. ANN simulates the biological nervous system and has become increasingly important in modeling and prediction, and the prediction of complex relationships between variables is impossible for other models such as logistic regression, which is useful in clinical diagnosis, especially cancer diagnosis [18,19]. Neural networks (radial basis function).…”

Section: Discussionmentioning

confidence: 99%

Differential diagnosis of lung cancer and tuberculosis based on 18F-fluorodeoxyglucose PET/CT multi-time points imaging

Luo

et al. 2023

Nuclear Medicine Communications

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Objective To investigate the value of 18F-fluorodeoxyglucose(FDG) PET/CT multi-time points imaging (MTPI) on the differential diagnosis between lung cancer (LC) and tuberculosis (TB). Methods Sixty-four patients underwent 18F-FDG PET/CT MTPI. The stdSUVmax, stdSUVavg, retention index, metabolic tumor volume, total lesion glycolysis at four-time points and slope of metabolic curve were measured and calculated, and the sex, age, and uniformity of FDG uptake were recorded. The difference in each index between LC and TB was analyzed, and dynamic metabolic curves (DMCs) of LC and TB were fitted by significance indexes. Artificial neural network (ANN) prediction models were established between squamous cell carcinoma (SCC) and TB, as well as between adenocarcinomas and TB. Results Differences between SCC and TB, stdSUVmax/avg at four-time points, total lesion glycolysis, stdSUVmax/avg slope (1–2 h,1–3 h and 1–4 h), uniformity of FDG uptake and age were significant. stdSUVavg has the largest area under the 4 h curve; age was only significant between adenocarcinomas and TB. DMCs at 1–4 h fitted by stdSUVavg were more helpful in differentiating LC and TB than stdSUVmax. stdSUVavg(1 h and 4 h), stdSUVavg slope 1–4 h, age, and uniformity of FDG uptake were selected to establish an ANN prediction model between SCC and TB; the area under the curve (AUC) was 100.0%. The same indices were used to establish the prediction model between adenocarcinomas and TB; the AUC was up to 83.5, and after adding stdSUVavg (2 and 4 h) to adenocarcinomas and TB models, the AUC was 87.7%. Conclusion 18F-FDG PET/CT MTPI fitting DMCs and establishing an ANN prediction model would distinguish SCC from TB relatively accurately and provide certain help in the differentiation between adenocarcinomas and TB.

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Section: Discussionmentioning

confidence: 99%

Differential diagnosis of lung cancer and tuberculosis based on 18F-fluorodeoxyglucose PET/CT multi-time points imaging

Luo

et al. 2023

Nuclear Medicine Communications

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“…An ANN estimates the impact on input variables and outcomes by increasing or decreasing the value of the connection weights between nodes through “learning”. Specifically, its ability to help predict outcomes is determined by the connections between neurons in the ANN [ 41 ]. Numerous studies have verified that the ANN model could categorize breast cancer patients using medical images [ 42 , 43 ].…”

Section: Methodsmentioning

confidence: 99%

Blood Test for Breast Cancer Screening through the Detection of Tumor-Associated Circulating Transcripts

Park

Ahn

Kim

et al. 2022

IJMS

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Liquid biopsy has been emerging for early screening and treatment monitoring at each cancer stage. However, the current blood-based diagnostic tools in breast cancer have not been sufficient to understand patient-derived molecular features of aggressive tumors individually. Herein, we aimed to develop a blood test for the early detection of breast cancer with cost-effective and high-throughput considerations in order to combat the challenges associated with precision oncology using mRNA-based tests. We prospectively evaluated 719 blood samples from 404 breast cancer patients and 315 healthy controls, and identified 10 mRNA transcripts whose expression is increased in the blood of breast cancer patients relative to healthy controls. Modeling of the tumor-associated circulating transcripts (TACTs) is performed by means of four different machine learning techniques (artificial neural network (ANN), decision tree (DT), logistic regression (LR), and support vector machine (SVM)). The ANN model had superior sensitivity (90.2%), specificity (80.0%), and accuracy (85.7%) compared with the other three models. Relative to the value of 90.2% achieved using the TACT assay on our test set, the sensitivity values of other conventional assays (mammogram, CEA, and CA 15-3) were comparable or much lower, at 89%, 7%, and 5%, respectively. The sensitivity, specificity, and accuracy of TACTs were appreciably consistent across the different breast cancer stages, suggesting the potential of the TACTs assay as an early diagnosis and prediction of poor outcomes. Our study potentially paves the way for a simple and accurate diagnostic and prognostic tool for liquid biopsy.

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“…ANN is a non-linear, self-learning system composed of numerous independent processing units [ 11 ], and can therefore process a large amount of data simultaneously [ 12 ]. In recent years, deep neural network (DNN) models made significant achievements in numerous computational biology and medicine problems [ 13 , 14 ]. In addition, several research groups have utilized ANN to construct prediction models for EFW.…”

Section: Introductionmentioning

confidence: 99%

Fetal weight estimation based on deep neural network: a retrospective observational study

Wang

Shi

Zhang

et al. 2023

BMC Pregnancy Childbirth

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Background Improving the accuracy of estimated fetal weight (EFW) calculation can contribute to decision-making for obstetricians and decrease perinatal complications. This study aimed to develop a deep neural network (DNN) model for EFW based on obstetric electronic health records. Methods This study retrospectively analyzed the electronic health records of pregnant women with live births delivery at the obstetrics department of International Peace Maternity & Child Health Hospital between January 2016 and December 2018. The DNN model was evaluated using Hadlock’s formula and multiple linear regression. Results A total of 34824 live births (23922 primiparas) from 49896 pregnant women were analyzed. The root-mean-square error of DNN model was 189.64 g (95% CI 187.95 g—191.16 g), and the mean absolute percentage error was 5.79% (95%CI: 5.70%—5.81%), significantly lower compared to Hadlock’s formula (240.36 g and 6.46%, respectively). By combining with previously unreported factors, such as birth weight of prior pregnancies, a concise and effective DNN model was built based on only 10 parameters. Accuracy rate of a new model increased from 76.08% to 83.87%, with root-mean-square error of only 243.80 g. Conclusions Proposed DNN model for EFW calculation is more accurate than previous approaches in this area and be adopted for better decision making related to fetal monitoring.

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Building and Interpreting Artificial Neural Network Models for Biological Systems

Cited by 6 publications

References 21 publications

Differential diagnosis of lung cancer and tuberculosis based on 18F-fluorodeoxyglucose PET/CT multi-time points imaging

Differential diagnosis of lung cancer and tuberculosis based on 18F-fluorodeoxyglucose PET/CT multi-time points imaging

Blood Test for Breast Cancer Screening through the Detection of Tumor-Associated Circulating Transcripts

Fetal weight estimation based on deep neural network: a retrospective observational study

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