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In this era, the wide application of various machine learning (ML) techniques and/or the employment of deep learning (DL) algorithms in the sphere of healthcare services has accelerated the computational procedure of detection along with the commencement of early diagnosis of different diseases/disorders to enhance the survival rate of human beings as well as prolong the lifetime of the patients. Several ML and DL methods including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Convolutional Neural Network (CNN) are rapidly deployed to detect the presence of disease and diagnose the disease for shielding the human organs. Table 1 explores some related research works regarding the usage of the principles of ML and DL for the diseases responsible for affecting various human organs.
Table 1. Computational approaches of ML and DL for detecting and diagnosing disease
Employed ML approaches Associated organ Advantages Future challenges Reference Neural Networks, Logistic Regression Head and Neck Effective diagnosis of head and neck cancer-affected patients. Producing better diagnostic accuracy considering smoking history along with the perineural invasion of patients. [126] DT, RF Heart Heart disease detection with high accuracy. A diverse mixture of ML techniques to predict heart diseases with better accuracy. [127] KNN Liver Early and effective prediction of chronic liver infections. Development of a model for obtaining more accuracy to detect chronic liver infections. [128] DL Pancreas DL-based Nucleus Classification of images for predicting pancreas cancer with high accuracy. Employment of a larger dataset to produce better accuracy for predicting pancreas cancer. [129] Bayes classifiers, SVM Stomach Creation of a model by dint of ML approaches to initially detect stomach cancer. Avoid the statistical assumptions and consideration of larger datasets to detect stomach cancer with better accuracy. [130] LR, RF Kidney Effective diagnosis of chronic kidney diseases (CKD) with superior accuracy. Improvement in diagnosis of CKD with better accuracy considering more categories of severe CKD and more complex data samples of patients. [131] CNN Lung Lung cancer prediction from data of CT images. Consideration of the smoking history of patients. [132] Shallow convolutional
neural networkBreast Breast cancer identification with higher accuracy. This works compared with only limited CNN and deep learning methods, while these number could be extended in future for better prospective and better efficiency. [133] DL Brain Effective diagnosis of brain cancer by highly efficient deep learning model depending upon CNN for glioblastoma multiforme (GBM) subtype detection
with superior accuracy.This work could be extended to utilize in other human cancers for designing DL-oriented diagnostic methods through more high-throughput experimental data profile. [134] DL/CNN Brain Explanation-driven DL model through the use of CNN, local interpretable model-agnostic explanation (LIME) as well as Shapley additive explanation (SHAP) to predict discrete subtypes of brain tumours from MRI image dataset. Classification techniques with higher accuracy and better optimizer could be applied and superimposed on proposed technique. [135] The current methods of manufacturing organoids are yet to demonstrate consistency and robustness. ML can help design and test organoids utilizing computers rather than the traditional lab method. Scalable production of high-grade organoids can be possible with the help of ML/DL. Mechano-transduction pathways can be applied to regulate the manufacturing of organoids and ML methods can help in the identification of key signature cytoskeleton states associated with the phenotype of the organoid.
doi: 10.3967/bes2023.083
A Comprehensive View on the Progress of Organoid Research with an Emphasis on its Relevance to Disease Characterization
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Wrote the manuscript: Chandra Kishore, Vaishali Ji, Ayan Mukherji, and Sinthia Roy Banerjee; Searched and read papers: Chandra Kishore, Vaishali Ji, and Ayan Mukherji; Revised the manuscript: Chandra Kishore, Saurav Mallik, Soumen Kumar Pati, Namrata Tomar, and Soumadip Ghosh; Supervised the manuscript: Saurav Mallik, Aimin Li, and Raza Ali Naqvi.
None of the authors have competing interests to declare.
注释:1) AUTHOR CONTRIBUTIONS: 2) COMPETING INTERESTS: -
Figure 1. Fundamental chart of various categories of human tissue-specific organoids along with relevant factors. Inducers and/or differentiation factors might vary depending upon on the starting material (viz., iPSC/Adult stem cells/iPSC cell line), while iPSCs refers to “induced pluripotent stem cells”.
Table 1. Computational approaches of ML and DL for detecting and diagnosing disease
Employed ML approaches Associated organ Advantages Future challenges Reference Neural Networks, Logistic Regression Head and Neck Effective diagnosis of head and neck cancer-affected patients. Producing better diagnostic accuracy considering smoking history along with the perineural invasion of patients. [126] DT, RF Heart Heart disease detection with high accuracy. A diverse mixture of ML techniques to predict heart diseases with better accuracy. [127] KNN Liver Early and effective prediction of chronic liver infections. Development of a model for obtaining more accuracy to detect chronic liver infections. [128] DL Pancreas DL-based Nucleus Classification of images for predicting pancreas cancer with high accuracy. Employment of a larger dataset to produce better accuracy for predicting pancreas cancer. [129] Bayes classifiers, SVM Stomach Creation of a model by dint of ML approaches to initially detect stomach cancer. Avoid the statistical assumptions and consideration of larger datasets to detect stomach cancer with better accuracy. [130] LR, RF Kidney Effective diagnosis of chronic kidney diseases (CKD) with superior accuracy. Improvement in diagnosis of CKD with better accuracy considering more categories of severe CKD and more complex data samples of patients. [131] CNN Lung Lung cancer prediction from data of CT images. Consideration of the smoking history of patients. [132] Shallow convolutional
neural networkBreast Breast cancer identification with higher accuracy. This works compared with only limited CNN and deep learning methods, while these number could be extended in future for better prospective and better efficiency. [133] DL Brain Effective diagnosis of brain cancer by highly efficient deep learning model depending upon CNN for glioblastoma multiforme (GBM) subtype detection
with superior accuracy.This work could be extended to utilize in other human cancers for designing DL-oriented diagnostic methods through more high-throughput experimental data profile. [134] DL/CNN Brain Explanation-driven DL model through the use of CNN, local interpretable model-agnostic explanation (LIME) as well as Shapley additive explanation (SHAP) to predict discrete subtypes of brain tumours from MRI image dataset. Classification techniques with higher accuracy and better optimizer could be applied and superimposed on proposed technique. [135] -
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