Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective

2020 ◽  
Vol 50 (6) ◽  
pp. 541-548 ◽  
Author(s):  
Jérémy Dana ◽  
Vincent Agnus ◽  
Farid Ouhmich ◽  
Benoit Gallix
2021 ◽  
Author(s):  
Sebastian Manuel Milluzzo ◽  
Paola Cesaro ◽  
Leonardo Minelli Grazioli ◽  
Nicola Olivari ◽  
Cristiano Spada

2019 ◽  
Vol 31 (4) ◽  
pp. 378-388 ◽  
Author(s):  
Yuichi Mori ◽  
Shin‐ei Kudo ◽  
Hussein E. N. Mohmed ◽  
Masashi Misawa ◽  
Noriyuki Ogata ◽  
...  

2021 ◽  
Vol 10 (16) ◽  
pp. 3527
Author(s):  
Hsu-Heng Yen ◽  
Ping-Yu Wu ◽  
Mei-Fen Chen ◽  
Wen-Chen Lin ◽  
Cheng-Lun Tsai ◽  
...  

With the decreasing incidence of peptic ulcer bleeding (PUB) over the past two decades, the clinician experience of managing patients with PUB has also declined, especially for young endoscopists. A patient with PUB management requires collaborative care involving the emergency department, gastroenterologist, radiologist, and surgeon, from initial assessment to hospital discharge. The application of artificial intelligence (AI) methods has remarkably improved people’s lives. In particular, AI systems have shown great potential in many areas of gastroenterology to increase human performance. Colonoscopy polyp detection or diagnosis by an AI system was recently introduced for commercial use to improve endoscopist performance. Although PUB is a longstanding health problem, these newly introduced AI technologies may soon impact endoscopists’ clinical practice by improving the quality of care for these patients. To update the current status of AI application in PUB, we reviewed recent relevant literature and provided future perspectives that are required to integrate such AI tools into real-world practice.


Author(s):  
Masashi Misawa ◽  
Shin‐ei Kudo ◽  
Yuichi Mori ◽  
Yasuharu Maeda ◽  
Yushi Ogawa ◽  
...  

2019 ◽  
Vol 20 (12) ◽  
pp. 1227-1243
Author(s):  
Hina Qamar ◽  
Sumbul Rehman ◽  
D.K. Chauhan

Cancer is the second leading cause of morbidity and mortality worldwide. Although chemotherapy and radiotherapy enhance the survival rate of cancerous patients but they have several acute toxic effects. Therefore, there is a need to search for new anticancer agents having better efficacy and lesser side effects. In this regard, herbal treatment is found to be a safe method for treating and preventing cancer. Here, an attempt has been made to screen some less explored medicinal plants like Ammania baccifera, Asclepias curassavica, Azadarichta indica, Butea monosperma, Croton tiglium, Hedera nepalensis, Jatropha curcas, Momordica charantia, Moringa oleifera, Psidium guajava, etc. having potent anticancer activity with minimum cytotoxic value (IC50 >3μM) and lesser or negligible toxicity. They are rich in active phytochemicals with a wide range of drug targets. In this study, these medicinal plants were evaluated for dose-dependent cytotoxicological studies via in vitro MTT assay and in vivo tumor models along with some more plants which are reported to have IC50 value in the range of 0.019-0.528 mg/ml. The findings indicate that these plants inhibit tumor growth by their antiproliferative, pro-apoptotic, anti-metastatic and anti-angiogenic molecular targets. They are widely used because of their easy availability, affordable price and having no or sometimes minimal side effects. This review provides a baseline for the discovery of anticancer drugs from medicinal plants having minimum cytotoxic value with minimal side effects and establishment of their analogues for the welfare of mankind.


2019 ◽  
Vol 31 (4) ◽  
pp. 363-371 ◽  
Author(s):  
Shin‐ei Kudo ◽  
Yuichi Mori ◽  
Masashi Misawa ◽  
Kenichi Takeda ◽  
Toyoki Kudo ◽  
...  

Author(s):  
Jiawei Wang ◽  
Yu Zhang ◽  
Niloofar Heshmati Aghda ◽  
Amit Raviraj Pillai ◽  
Rishi Thakkar ◽  
...  

Author(s):  
Florian A. Huber ◽  
Roman Guggenberger

AbstractRecent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.


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