Deep learning-based artificial intelligence applications in prostate MRI: brief summary

2021 ◽  
Author(s):  
Baris Turkbey ◽  
Masoom A. Haider
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Diagnostic Performance ◽  
Lesion Detection ◽  
Western World ◽  
Complex Nature ◽  
Cancer Type ◽  
Prostate Mri ◽  
Prostate Segmentation ◽  
Established Role

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.

scholarly journals Artificial Intelligence-Based Polyp Detection in Colonoscopy: Where Have We Been, Where Do We Stand, and Where Are We Headed?

2020 ◽  
Vol 36 (6) ◽  
pp. 428-438
Author(s):  
Thomas Wittenberg ◽  
Martin Raithel
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Lesion Detection ◽  
Screening Colonoscopy ◽  
Computer Assisted ◽  
Polyp Detection ◽  
The Past

<b><i>Background:</i></b> In the past, image-based computer-assisted diagnosis and detection systems have been driven mainly from the field of radiology, and more specifically mammography. Nevertheless, with the availability of large image data collections (known as the “Big Data” phenomenon) in correlation with developments from the domain of artificial intelligence (AI) and particularly so-called deep convolutional neural networks, computer-assisted detection of adenomas and polyps in real-time during screening colonoscopy has become feasible. <b><i>Summary:</i></b> With respect to these developments, the scope of this contribution is to provide a brief overview about the evolution of AI-based detection of adenomas and polyps during colonoscopy of the past 35 years, starting with the age of “handcrafted geometrical features” together with simple classification schemes, over the development and use of “texture-based features” and machine learning approaches, and ending with current developments in the field of deep learning using convolutional neural networks. In parallel, the need and necessity of large-scale clinical data will be discussed in order to develop such methods, up to commercially available AI products for automated detection of polyps (adenoma and benign neoplastic lesions). Finally, a short view into the future is made regarding further possibilities of AI methods within colonoscopy. <b><i>Key Messages:</i></b> Research<b><i></i></b>of<b><i></i></b>image-based lesion detection in colonoscopy data has a 35-year-old history. Milestones such as the Paris nomenclature, texture features, big data, and deep learning were essential for the development and availability of commercial AI-based systems for polyp detection.

Deep‐Learning‐Based Artificial Intelligence for PI‐RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study

2020 ◽  
Vol 52 (5) ◽  
pp. 1499-1507 ◽  
Author(s):  
Thomas Sanford ◽  
Stephanie A. Harmon ◽  
Evrim B. Turkbey ◽  
Deepak Kesani ◽  
Sena Tuncer ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Development Study ◽  
Prostate Mri ◽  
Multiparametric Prostate Mri

scholarly journals Diagnostic Performance of Endoscopic Ultrasound-Artificial Intelligence Using Deep Learning Analysis of Gallbladder Polypoid Lesions: A Feasibility Study

2021 ◽  
Author(s):  
Sung Ill Jang ◽  
Young Jae Kim ◽  
Eui Joo Kim ◽  
Huapyong Kang ◽  
Seung Jin Shon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Endoscopic Ultrasound ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Validation Cohort ◽  
External Validation ◽  
Diagnostic Modality ◽  
Internal Validation

Abstract Endoscopic ultrasound (EUS) is the most accurate diagnostic modality for polypoid lesions of the gallbladder (GB), but is limited by subjective interpretation. We evaluated the diagnostic performance of deep learning-based artificial intelligence (AI) in differentiating polypoid lesions using EUS images. The diagnostic performance of the EUS-AI system with ResNet50 architecture was evaluated via three processes: training, internal validation, and testing. The diagnostic performance was also verified using an external validation cohort and compared with the performance of EUS endoscopists. In the AI development cohort, the diagnostic performance of EUS-AI including sensitivity, specificity, positive predictive value, negative predictive value and accuracy. For the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values for EUS-AI were 77.8%, 91.6%, 57.9%, 96.5%, and 89.8%, respectively. In the external validation cohort, the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values were 60.3%, 77.4%, 36.2%, 90.2%, and 74.4%, respectively, for EUS-AI; they were 74.2%, 44.9%, 75.4%, 46.2%, and 65.3%, respectively, for the endoscopists. The accuracy of the EUS-AI was between the accuracies of mid-level (66.7%) and expert EUS endoscopists (77.5%). This EUS-AI system showed favorable performance for the diagnosis of neoplastic GB polyps, with a performance comparable to that of EUS endoscopists.

scholarly journals Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection

Radiology ◽  
2018 ◽  
Vol 289 (1) ◽  
pp. 160-169 ◽  
Author(s):  
Fang Liu ◽  
Zhaoye Zhou ◽  
Alexey Samsonov ◽  
Donna Blankenbaker ◽  
Will Larison ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Cartilage Lesion ◽  
Lesion Detection ◽  
Learning Approach ◽  
Mr Images
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