Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success

Thrall, James H.; Li, Xiang; Li, Quanzheng; Cruz, Cinthia; Do, Synho; Dreyer, Keith J.; Brink, James A.

doi:10.1016/j.jacr.2017.12.026

Cited by 555 publications

(302 citation statements)

References 12 publications

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“…There are several thorough reviews and overviews of the field to consult for more information, across modalities and organs, and with different points of view and level of technical details. For example the comprehensive review [102] 27 , covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [103,104,105,106,107,108,109,110,111,112], including radiomics and imaging genomics (radiogenomics) [113], and toolkits and libraries for deep learning [114]; deep learning in neuroimaging and neuroradiology [115]; brain segmentation [116]; stroke imaging [117,118]; neuropsychiatric disorders [119]; breast cancer [120,121]; chest imaging [122]; imaging in oncology [123,124,125]; medical ultrasound [126,127]; and more technical surveys of deep learning in medical image analysis [42,128,129,130]. Finally, for those who like to be hands-on, there are many instructive introductory deep learning tutorials available online.…”

Section: Deep Learning Medical Imaging and Mrimentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,591

883

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What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging. Artificial neural networksArtificial neural networks (ANNs) is one of the most famous machine learning models, introduced already in the 1950s, and actively studied since [41, Chapter 1.2]. 11 Roughly, a neural network consists of a number of connected computational units, called neurons, arranged in layers. There's an input layer where data enters the network, followed by one or more hidden layers transforming the data as it flows through, before ending at an output layer that produces the neural network's predictions. The network is trained to output useful predictions by identifying patterns in a set of labeled training data, fed through the network while the outputs are compared with the actual labels by an objective function. During training the network's parametersthe strength of each neuron-is tuned until the patterns identified by the network result in good 10 https://www.deeplearningbook.org/ 11 The loose connection between artificial neural networks and neural networks in the brain is often mentioned, but quite over-blown considering the complexity of biological neural networks. However, there is some interesting recent work connecting neuroscience and artificial neural networks, indicating an increase in the cross-fertilization between the two fields [43,44,45].3 predictions for the training data. Once the patterns are learned, the network can be used to make predictions on new, unseen data, i.e. generalize to new data.It h...

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Section: Deep Learning Medical Imaging and Mrimentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,591

883

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“…At last, a number of cognitive biases may adversely affect the accuracy of a radiologists report of a glioma [31]. In order to reduce reporting time and cognitive biases, both of which may lead to reporting and diagnostic errors, radiomics offers a significant advantage [32], particularly in the context of general radiologists who may lack expertise in neuro-oncology. Nevertheless, the current radiomic strategy involves too much pre-and postprocess before the suitable machine learning model is established, more studies focusing on the efficacy-cost balance of such a machine learning system should be further conducted before its clinical application.…”

Section: Discussionmentioning

confidence: 99%

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

Zhao

Feng

et al. 2020

BMC Neurol

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Background: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy.Methods: Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. Results: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. Conclusions: Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

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“…Consequently, these transformations may improve risk assessment, diagnostic, and prognostic capabilities for several diseases. In this context, AI tools may help to extract more and better information from the patient in order to achieve accurate outcomes at lower health costs …”

Section: Applications In Healthcarementioning

confidence: 99%

“…Thus, radiomic analysis may be simply defined as an extraction of quantitative features or parameters, measurable and mineable from radiological images. Therefore, hundreds of abstract mathematical features, generally not extractable by the human eye, can be defined or detected on imaging modalities by using software . Figure demonstrates the necessary steps in a radiomics study.…”

Section: Radiomics and DL Applications In Radiologymentioning

confidence: 99%

“…These imaging features may aid in the early detection of malignant or many bone diseases. They can also be used to predict treatment response to therapies, including oncotherapy, and to estimate functional parameters . The combination of these imaging features with other clinical and genetic data may improve the capacity of detecting and predicting diagnosis and outcomes.…”

Section: Ai Revolutionizing Oral Health Carementioning

confidence: 99%

See 1 more Smart Citation

Radiomics and Machine Learning in Oral Healthcare

Leite

Vasconcelos

Willems

et al. 2020

Proteomics Clinical Apps

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The increasing storage of information, data, and forms of knowledge has led to the development of new technologies that can help to accomplish complex tasks in different areas, such as in dentistry. In this context, the role of computational methods, such as radiomics and Artificial Intelligence (AI) applications, has been progressing remarkably for dentomaxillofacial radiology (DMFR). These tools bring new perspectives for diagnosis, classification, and prediction of oral diseases, treatment planning, and for the evaluation and prediction of outcomes, minimizing the possibilities of human errors. A comprehensive review of the state-of-the-art of using radiomics and machine learning (ML) for imaging in oral healthcare is presented in this paper. Although the number of published studies is still relatively low, the preliminary results are very promising and in a near future, an augmented dentomaxillofacial radiology (ADMFR) will combine the use of radiomics-based and AI-based analyses with the radiologist's evaluation. In addition to the opportunities and possibilities, some challenges and limitations have also been discussed for further investigations.

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Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success

Cited by 555 publications

References 12 publications

An overview of deep learning in medical imaging focusing on MRI

An overview of deep learning in medical imaging focusing on MRI

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

Radiomics and Machine Learning in Oral Healthcare

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