scholarly journals Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer

2021 ◽  
Vol 22 ◽  
Author(s):  
Kiwook Kim ◽  
Sungwon Kim ◽  
Kyunghwa Han ◽  
Heejin Bae ◽  
Jaeseung Shin ◽  
...  
Author(s):  
Baris Turkbey ◽  
Masoom A. Haider

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.


2021 ◽  
Author(s):  
Jacob Johnson ◽  
Kaneel Senevirathne ◽  
Lawrence Ngo

In this work, we report the results of a deep-learning based liver lesion detection algorithm. While several liver lesion segmentation and classification algorithms have been developed, none of the previous work has focused on detecting suspicious liver lesions. Furthermore, their generalizability remains a pitfall due to their small sample size and sample homogeneity. Here, we developed and validated a highly generalizable deep-learning algorithm for detection of suspicious liver lesions. The algorithm was trained and tested on a diverse dataset containing CT exams from over 2,000 hospital sites in the United States. Our final model achieved an AUROC of 0.84 with a specificity of 0.99 while maintaining a sensitivity of 0.33.


Radiology ◽  
2018 ◽  
Vol 289 (1) ◽  
pp. 160-169 ◽  
Author(s):  
Fang Liu ◽  
Zhaoye Zhou ◽  
Alexey Samsonov ◽  
Donna Blankenbaker ◽  
Will Larison ◽  
...  

2007 ◽  
Vol 23 (6) ◽  
pp. 477
Author(s):  
Sang Chul Yun ◽  
Hyung Chul Kim ◽  
Chong Woo Chu ◽  
Eung Jin Shin ◽  
Moo Jun Baek ◽  
...  

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