Machine learning in medical applications: A review of state-of-the-art methods

Shehab, Mohammad; Abualigah, Laith; Shambour, Qusai; Abu-Hashem, Muhannad A.; Shambour, Mohd Khaled Yousef; Alsalibi, Ahmed Izzat; Gandomi, Amir H.

doi:10.1016/j.compbiomed.2022.105458

Cited by 274 publications

(140 citation statements)

References 163 publications

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“…As a branch of AI, ML could perform tasks without explicit programming instructions, discover hidden relationships between data, and perform data analysis. The wide application of ML methods in the field of pathology makes the technology direction appear diversified [ 2 ]. Commonly used ML methods have their own advantages and disadvantages.…”

Section: Discussionmentioning

confidence: 99%

“…The traditional ML algorithms include logistic regression, linear regression, decision tree (DT), naive bayes (NB), random forest (RF), support vector machine (SVM), multi-layer perceptron (MLP) and so on. In the medical field, ML could be applied to cluster patient characteristics, infer the probability of disease outcomes, etc [ 2 ]. ML could perform tasks without explicit programming instructions, discover hidden relationships between data, and perform data analysis.…”

Section: Introductionmentioning

confidence: 99%

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Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network

et al. 2022

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Objectives This paper aimed to identify the technology frontiers of artificial intelligence-assisted pathology based on patent citation network. Methods Patents related to artificial intelligence-assisted pathology were searched and collected from the Derwent Innovation Index (DII), which were imported into Derwent Data Analyzer (DDA, Clarivate Derwent, New York, NY, USA) for authority control, and imported into the freely available computer program Ucinet 6 for drawing the patent citation network. The patent citation network according to the citation relationship could describe the technology development context in the field of artificial intelligence-assisted pathology. The patent citations were extracted from the collected patent data, selected highly cited patents to form a co-occurrence matrix, and built a patent citation network based on the co-occurrence matrix in each period. Text clustering is an unsupervised learning method, an important method in text mining, where similar documents are grouped into clusters. The similarity between documents are determined by calculating the distance between them, and the two documents with the closest distance are combined. The method of text clustering was used to identify the technology frontiers based on the patent citation network, which was according to co-word analysis of the title and abstract of the patents in this field. Results 1704 patents were obtained in the field of artificial intelligence-assisted pathology, which had been currently undergoing three stages, namely the budding period (1992–2000), the development period (2001–2015), and the rapid growth period (2016–2021). There were two technology frontiers in the budding period (1992–2000), namely systems and methods for image data processing in computerized tomography (CT), and immunohistochemistry (IHC), five technology frontiers in the development period (2001–2015), namely spectral analysis methods of biomacromolecules, pathological information system, diagnostic biomarkers, molecular pathology diagnosis, and pathological diagnosis antibody, and six technology frontiers in the rapid growth period (2016–2021), namely digital pathology (DP), deep learning (DL) algorithms—convolutional neural networks (CNN), disease prediction models, computational pathology, pathological image analysis method, and intelligent pathological system. Conclusions Artificial intelligence-assisted pathology was currently in a rapid development period, and computational pathology, DL and other technologies in this period all involved the study of algorithms. Future research hotspots in this field would focus on algorithm improvement and intelligent diagnosis in order to realize the precise diagnosis. The results of this study presented an overview of the characteristics of research status and development trends in the field of artificial intelligence-assisted pathology, which could help readers broaden innovative ideas and discover new technological opportunities, and also served as important indicators for government policymaking.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network

et al. 2022

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“…Broadly speaking, a classifier uses the input data to find out relationships that can be used to determine the class where the input data belongs to. The evaluation of the classifier is done using three basic measurements: accuracy, specificity, and sensitivity [ 106 , 107 ]. Accuracy refers to the percentage of images that are correctly classified in their corresponding classes; sensitivity is the percentage of classified images as malignant that truly are specificity is the percentage of classified images as benign that truly are, and the area under the curve is a parameter that allows choosing the optimal models.…”

Section: Image Processing and Classification Strategiesmentioning

confidence: 99%

“…It takes a value between 0 and 1, being a good classifier the one that has a value close to 1 [ 108 ]. In this sense, depending on the training algorithm required by the strategy, classifiers can be divided in unsupervised or supervised [ 45 , 106 , 107 ].…”

Section: Image Processing and Classification Strategiesmentioning

confidence: 99%

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Basurto-Hurtado

Cruz-Albarran

Toledano‐Ayala

et al. 2022

Cancers

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Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

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“…The manual embryonic analysis is time-consuming and requires continuous keen observations and subject knowledge. In the current era of machine learning and artificial intelligence (AI), deep-learning-based methods aid humans with several medical applications [ 17 ]. Thus, AI can help in the assessment of sperm, embryos, and oocytes to improve the success rate of IVF [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network for Embryonic Analysis

et al. 2022

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Infertility is one of the most important health concerns worldwide. It is characterized by not being successful of pregnancy after some periods of periodic unprotected sexual intercourse. In vitro fertilization (IVF) is an assisted reproduction technique that efficiently addresses infertility. IVF replaces the actual mode of reproduction through a manual procedure wherein embryos are cultivated in a controlled laboratory environment until they reach the blastocyst stage. The standard IVF procedure includes the transfer of one or two blastocysts from several blastocysts that are grown in a controlled environment. The morphometric properties of blastocysts with their compartments such as trophectoderm (TE), zona pellucida (ZP), inner cell mass (ICM), and blastocoel (BL), are analyzed through manual microscopic analysis to predict viability. Deep learning has been extensively used for medical diagnosis and analysis and can be a powerful tool to automate the morphological analysis of human blastocysts. However, the existing approaches are inaccurate and require extensive preprocessing and expensive architectures. Thus, to cope with the automatic detection of blastocyst components, this study proposed a novel multiscale aggregation semantic segmentation network (MASS-Net) that combined four different scales via depth-wise concatenation. The extensive use of depthwise separable convolutions resulted in a decrease in the number of trainable parameters. Further, the innovative multiscale design provided rich spatial information of different resolutions, thereby achieving good segmentation performance without a very deep architecture. MASS-Net utilized 2.06 million trainable parameters and accurately detects TE, ZP, ICM, and BL without using preprocessing stages. Moreover, it can provide a separate binary mask for each blastocyst component simultaneously, and these masks provide the structure of each component for embryonic analysis. Further, the proposed MASS-Net was evaluated using publicly available human blastocyst (microscopic) imaging data. The experimental results revealed that it can effectively detect TE, ZP, ICM, and BL with mean Jaccard indices of 79.08, 84.69, 85.88%, and 89.28%, respectively, for embryological analysis, which was higher than those of the state-of-the-art methods.

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Machine learning in medical applications: A review of state-of-the-art methods

Cited by 274 publications

References 163 publications

Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network

Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network for Embryonic Analysis

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