|
| Applications | Study aims | Outcomes | References |
|
| Ultrasound imaging | Development of deep learning detection network for ultrasonic equipment for real-time detection of breast cancer. | Method to realize the intelligence of the low-computation-power ultrasonic equipment, and real-time assistance for detection of breast lesions was developed. | [12].................. |
|
| CT imaging | To perform a quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. | DLIR improved CT evaluation of the abdomen in the portal venous phase. DLIR strength should be chosen to balance the degree of desired denoising for a clinical task relative to mild blurring. | [13].................. |
|
| MRI | To develop a deep learning algorithm for automated detection and localization of intracranial aneurysms on time-of-flight MR angiography and evaluate its diagnostic performance. | A deep learning algorithm detected intracranial aneurysms with a high diagnostic performance which was validated using an external data set. | [14].................. |
|
| Cancer diagnosis | To conduct the breast cancer diagnosis by using principal component analysis-support vector machine (PCA-SVM) and principal component analysis-linear discriminant analysis-support vector machine (PCA-LDA-SVM) model classifier algorithms (LabVIEW). | The proposed method provides improvement especially for the polynomial kernel function. An increase in classification accuracy was observed in the test phase compared to PCA-SVM, along with improved classification. | [15].................. |
|
| Cancer diagnosis | To develop a computerized image analysis system using deep learning for the detection of esophageal and esophagogastric junctional (E/J) adenocarcinoma. | AI system achieved high sensitivity and acceptable specificity for the detection of E/J cancers and may be a good supporting tool for the screening of E/J cancers. | [16].................. |
|
| Cancer diagnosis | To study whether an artificial intelligence (AI) system can increase the accuracy of characterizations of polyps by endoscopists of different skill levels. | The method significantly increased the accuracy of evaluation of diminutive colorectal polyps and reduced the time of diagnosis by endoscopists. | [17].................. |
|
| Drug development | To study whether recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. | Recurrent neural networks based on the long short-term memory (LSTM) can be applied to learn a statistical chemical language model. The model can generate large sets of novel molecules with physicochemical properties that are similar to the training molecules ones. | [18].................. |
|
| Genomics | To validate the ability of a computational approach based on deep neural networks (DeepCpG) to predict methylation states in single cells. | DeepCpG yields substantially more accurate predictions than old methods. It was shown that the model parameters can be interpreted, thereby providing insights into how sequence composition affects methylation variability. | [19].................. |
|
