Literature DB >> 31898014

AI-based computer-aided diagnosis (AI-CAD): the latest review to read first.

Hiroshi Fujita1.   

Abstract

The third artificial intelligence (AI) boom is coming, and there is an inkling that the speed of its evolution is quickly increasing. In games like chess, shogi, and go, AI has already defeated human champions, and the fact that it is able to achieve autonomous driving is also being realized. Under these circumstances, AI has evolved and diversified at a remarkable pace in medical diagnosis, especially in diagnostic imaging. Therefore, this commentary focuses on AI in medical diagnostic imaging and explains the recent development trends and practical applications of computer-aided detection/diagnosis using artificial intelligence, especially deep learning technology, as well as some topics surrounding it.

Entities:  

Keywords:  Artificial intelligence (AI); Computer-aided detection/diagnosis (CAD); Convolutional neural network; Deep learning; Machine learning; Medical image recognition

Year:  2020        PMID: 31898014     DOI: 10.1007/s12194-019-00552-4

Source DB:  PubMed          Journal:  Radiol Phys Technol        ISSN: 1865-0333


  46 in total

Review 1.  Computer-aided simple triage.

Authors:  Roman Goldenberg; Nathan Peled
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-04-16       Impact factor: 2.924

Review 2.  Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging.

Authors:  Shizuo Kaji; Satoshi Kida
Journal:  Radiol Phys Technol       Date:  2019-06-20

3.  Radiogenomics: bridging imaging and genomics.

Authors:  Zuhir Bodalal; Stefano Trebeschi; Thi Dan Linh Nguyen-Kim; Winnie Schats; Regina Beets-Tan
Journal:  Abdom Radiol (NY)       Date:  2019-06

Review 4.  Overview of deep learning in medical imaging.

Authors:  Kenji Suzuki
Journal:  Radiol Phys Technol       Date:  2017-07-08

Review 5.  Why CAD Failed in Mammography.

Authors:  Ajay Kohli; Saurabh Jha
Journal:  J Am Coll Radiol       Date:  2018-02-03       Impact factor: 5.532

6.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

Authors:  Babak Ehteshami Bejnordi; Mitko Veta; Paul Johannes van Diest; Bram van Ginneken; Nico Karssemeijer; Geert Litjens; Jeroen A W M van der Laak; Meyke Hermsen; Quirine F Manson; Maschenka Balkenhol; Oscar Geessink; Nikolaos Stathonikos; Marcory Crf van Dijk; Peter Bult; Francisco Beca; Andrew H Beck; Dayong Wang; Aditya Khosla; Rishab Gargeya; Humayun Irshad; Aoxiao Zhong; Qi Dou; Quanzheng Li; Hao Chen; Huang-Jing Lin; Pheng-Ann Heng; Christian Haß; Elia Bruni; Quincy Wong; Ugur Halici; Mustafa Ümit Öner; Rengul Cetin-Atalay; Matt Berseth; Vitali Khvatkov; Alexei Vylegzhanin; Oren Kraus; Muhammad Shaban; Nasir Rajpoot; Ruqayya Awan; Korsuk Sirinukunwattana; Talha Qaiser; Yee-Wah Tsang; David Tellez; Jonas Annuscheit; Peter Hufnagl; Mira Valkonen; Kimmo Kartasalo; Leena Latonen; Pekka Ruusuvuori; Kaisa Liimatainen; Shadi Albarqouni; Bharti Mungal; Ami George; Stefanie Demirci; Nassir Navab; Seiryo Watanabe; Shigeto Seno; Yoichi Takenaka; Hideo Matsuda; Hady Ahmady Phoulady; Vassili Kovalev; Alexander Kalinovsky; Vitali Liauchuk; Gloria Bueno; M Milagro Fernandez-Carrobles; Ismael Serrano; Oscar Deniz; Daniel Racoceanu; Rui Venâncio
Journal:  JAMA       Date:  2017-12-12       Impact factor: 56.272

7.  Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.

Authors:  Alejandro Rodríguez-Ruiz; Elizabeth Krupinski; Jan-Jurre Mordang; Kathy Schilling; Sylvia H Heywang-Köbrunner; Ioannis Sechopoulos; Ritse M Mann
Journal:  Radiology       Date:  2018-11-20       Impact factor: 11.105

Review 8.  New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence.

Authors:  Yiming Gao; Krzysztof J Geras; Alana A Lewin; Linda Moy
Journal:  AJR Am J Roentgenol       Date:  2019-02       Impact factor: 3.959

9.  Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.

Authors:  Ryan Poplin; Avinash V Varadarajan; Katy Blumer; Yun Liu; Michael V McConnell; Greg S Corrado; Lily Peng; Dale R Webster
Journal:  Nat Biomed Eng       Date:  2018-02-19       Impact factor: 25.671

10.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

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  18 in total

1.  External validation of AI algorithms in breast radiology: the last healthcare security checkpoint?

Authors:  Teodoro Martin-Noguerol; Antonio Luna
Journal:  Quant Imaging Med Surg       Date:  2021-06

Review 2.  A review on AI in PET imaging.

Authors:  Keisuke Matsubara; Masanobu Ibaraki; Mitsutaka Nemoto; Hiroshi Watabe; Yuichi Kimura
Journal:  Ann Nucl Med       Date:  2022-01-14       Impact factor: 2.668

3.  Tooth recognition of 32 tooth types by branched single shot multibox detector and integration processing in panoramic radiographs.

Authors:  Takumi Morishita; Chisako Muramatsu; Yuta Seino; Ryo Takahashi; Tatsuro Hayashi; Wataru Nishiyama; Xiangrong Zhou; Takeshi Hara; Akitoshi Katsumata; Hiroshi Fujita
Journal:  J Med Imaging (Bellingham)       Date:  2022-06-22

4.  A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms.

Authors:  Nagwan Abdel Samee; Amel A Alhussan; Vidan Fathi Ghoneim; Ghada Atteia; Reem Alkanhel; Mugahed A Al-Antari; Yasser M Kadah
Journal:  Sensors (Basel)       Date:  2022-06-30       Impact factor: 3.847

5.  Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images.

Authors:  Mahmood Alzubaidi; Marco Agus; Khalid Alyafei; Khaled A Althelaya; Uzair Shah; Alaa Abd-Alrazaq; Mohammed Anbar; Michel Makhlouf; Mowafa Househ
Journal:  iScience       Date:  2022-07-03

6.  Deep learning versus the human visual system for detecting motion blur in radiography.

Authors:  Rie Tanaka; Shiho Nozaki; Futa Goshima; Junji Shiraishi
Journal:  J Med Imaging (Bellingham)       Date:  2022-01-18

7.  Mutual stain conversion between Giemsa and Papanicolaou in cytological images using cycle generative adversarial network.

Authors:  Atsushi Teramoto; Ayumi Yamada; Tetsuya Tsukamoto; Yuka Kiriyama; Eiko Sakurai; Kazuya Shiogama; Ayano Michiba; Kazuyoshi Imaizumi; Kuniaki Saito; Hiroshi Fujita
Journal:  Heliyon       Date:  2021-02-24

Review 8.  Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine.

Authors:  Ryuji Hamamoto; Kruthi Suvarna; Masayoshi Yamada; Kazuma Kobayashi; Norio Shinkai; Mototaka Miyake; Masamichi Takahashi; Shunichi Jinnai; Ryo Shimoyama; Akira Sakai; Ken Takasawa; Amina Bolatkan; Kanto Shozu; Ai Dozen; Hidenori Machino; Satoshi Takahashi; Ken Asada; Masaaki Komatsu; Jun Sese; Syuzo Kaneko
Journal:  Cancers (Basel)       Date:  2020-11-26       Impact factor: 6.639

9.  Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network.

Authors:  Minghui Guo; Kangjian Wang; Shunlan Liu; Yongzhao Du; Peizhong Liu; Qichen Su; Guorong Lv
Journal:  Comput Intell Neurosci       Date:  2021-06-02

Review 10.  Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.

Authors:  Jasper J Twilt; Kicky G van Leeuwen; Henkjan J Huisman; Jurgen J Fütterer; Maarten de Rooij
Journal:  Diagnostics (Basel)       Date:  2021-05-26
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