Influence of content-based image retrieval on the accuracy and inter-reader agreement of usual interstitial pneumonia CT pattern classification.

Authors: Park S; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Hwang HJ; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. umttette@hanmail.net., Yun J; Department of Convergence Medicine, Biomedical Engineering Research Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Chae EJ; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Choe J; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Lee SM; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Lee HN; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Shin SY; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Park H; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Jeong H; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Kim MJ; Department of Pulmonology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Lee JH; Department of Pulmonology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Jo KW; Department of Pulmonology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Baek S; Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea., Seo JB; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Source: European radiology [Eur Radiol] 2025 Nov; Vol. 35 (11), pp. 7199-7212. Date of Electronic Publication: 2025 May 22.

Publication Type: Journal Article

Journal Info: Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE

MeSH Terms: Tomography, X-Ray Computed*/methods , Idiopathic Pulmonary Fibrosis*/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted*/methods , Lung Diseases, Interstitial*/diagnostic imaging , Information Storage and Retrieval*/methods, Humans ; Male ; Female ; Middle Aged ; Retrospective Studies ; Observer Variation ; Aged ; Reproducibility of Results ; Algorithms ; Deep Learning ; Adult ; Aged, 80 and over

Reference standard methodology in the clinical evaluation of AI chest X-ray algorithms for lung cancer detection: A systematic review.

Authors: Duncan SF; Digital Health Validation Lab, University of Glasgow, Imaging Centre of Excellence, Langlands Dr, Glasgow G51 4LB, UK; The Beatson West of Scotland Cancer Center, 1053 Great Western Rd, Glasgow G12 0YN, UK., Kidd AC; The Beatson West of Scotland Cancer Center, 1053 Great Western Rd, Glasgow G12 0YN, UK; School of Cancer Sciences, University of Glasgow, Garscube Estate, Switchback Road, Bearsden G61 1QH, UK., Lampignano JP; University of Glasgow Library, Hillhead St, Glasgow G12 8QE, UK; Radiology Department, NHS Greater Glasgow and Clyde, 1345 Govan Rd, Glasgow G51 4TF, UK., Cannon P; University of Glasgow Library, Hillhead St, Glasgow G12 8QE, UK., Hall M; Radiology Department, NHS Greater Glasgow and Clyde, 1345 Govan Rd, Glasgow G51 4TF, UK., Stobo DB; Radiology Department, NHS Greater Glasgow and Clyde, 1345 Govan Rd, Glasgow G51 4TF, UK., Maclay JD; Department of Respiratory Medicine, Glasgow Royal Infirmary, 84 Castle St, Glasgow G4 0SF, UK., Blyth KG; School of Cancer Sciences, University of Glasgow, Garscube Estate, Switchback Road, Bearsden G61 1QH, UK; CRUK Scotland Centre, Switchback Rd, Bearsden, Glasgow G61 1BD, UK; Glasgow Pleural Disease Unit, NHS Greater Glasgow and Clyde, 1345 Govan Rd, Glasgow G51 4TF, UK., Lowe DJ; Digital Health Validation Lab, University of Glasgow, Imaging Centre of Excellence, Langlands Dr, Glasgow G51 4LB, UK; Emergency Department, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, 1345 Govan Rd, Glasgow G51 4TF, UK. Electronic address: David.lowe@glasgow.ac.uk.

Source: European journal of radiology [Eur J Radiol] 2025 Nov; Vol. 192, pp. 112409. Date of Electronic Publication: 2025 Sep 06.

Publication Type: Journal Article; Systematic Review; Review

Journal Info: Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 8106411 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7727 (Electronic) Linking ISSN: 0720048X NLM ISO Abbreviation: Eur J Radiol Subsets: MEDLINE

MeSH Terms: Lung Neoplasms*/diagnostic imaging , Algorithms* , Radiography, Thoracic*/standards , Radiography, Thoracic*/methods , Artificial Intelligence*/standards , Radiographic Image Interpretation, Computer-Assisted*/standards , Radiographic Image Interpretation, Computer-Assisted*/methods, Humans ; Reference Standards ; Reproducibility of Results

CONSeg: Voxelwise Uncertainty Quantification for Glioma Segmentation Using Conformal Prediction.

Authors: Elyassirad D; From the Student Research Committee (D.B., B.G., M.V.), Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran., Gheiji B; From the Student Research Committee (D.B., B.G., M.V.), Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran., Vatanparast M; From the Student Research Committee (D.B., B.G., M.V.), Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran., Ahmadzadeh AM; Department of Radiology (A.M.A.), Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran., Faghani S; Radiology Informatics Lab (S.F.), Department of Radiology, Mayo Clinic, Rochester, Minnesota. Faghani.Shahriar@mayo.edu.

Source: AJNR. American journal of neuroradiology [AJNR Am J Neuroradiol] 2025 Dec 04; Vol. 46 (12), pp. 2553-2560. Date of Electronic Publication: 2025 Dec 04.

Publication Type: Journal Article

Journal Info: Publisher: American Society of Neuroradiology Country of Publication: United States NLM ID: 8003708 Publication Model: Electronic Cited Medium: Internet ISSN: 1936-959X (Electronic) Linking ISSN: 01956108 NLM ISO Abbreviation: AJNR Am J Neuroradiol Subsets: MEDLINE

MeSH Terms: Glioma*/diagnostic imaging , Glioma*/pathology , Brain Neoplasms*/diagnostic imaging , Brain Neoplasms*/pathology , Magnetic Resonance Imaging*/methods , Image Interpretation, Computer-Assisted*/methods, Humans ; Uncertainty ; Reproducibility of Results ; Algorithms ; Sensitivity and Specificity

A multi-technique ensemble model leveraging attention mechanism and image processing for enhanced colorectal tumor detection.

Authors: Dharshini BL; Vellore Institute of Technology, Chennai, India., Arivarasi A; Vellore Institute of Technology, Chennai, India. arivarasi.a@vit.ac.in., Prashanth Kumar B; Vellore Institute of Technology, Chennai, India., Varsha JK; Vellore Institute of Technology, Chennai, India., Pavithraa S; Vellore Institute of Technology, Chennai, India.

Source: Scientific reports [Sci Rep] 2025 Dec 03; Vol. 15 (1), pp. 43041. Date of Electronic Publication: 2025 Dec 03.

Publication Type: Journal Article

Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

MeSH Terms: Colorectal Neoplasms*/diagnosis , Colorectal Neoplasms*/diagnostic imaging , Colorectal Neoplasms*/pathology , Image Processing, Computer-Assisted*/methods , Image Interpretation, Computer-Assisted*/methods, Humans ; Neural Networks, Computer ; Deep Learning ; Algorithms

Artificial intelligence-enhanced three-dimensional echocardiography reveals left atrial-ventricular coupling index as a novel prognostic marker in coronary artery disease.

Authors: Qin S; Department of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China., Li D; Department of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China., Feng Q; Department of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China., Zhang Y; Department of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China., Zhou D; Department of Cardiovascular Medicine, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China. cmu4h-zdh@126.com., Liu S; Department of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China. liushuang@cmu.edu.cn.

Source: BMC cardiovascular disorders [BMC Cardiovasc Disord] 2025 Dec 03; Vol. 25 (1), pp. 859. Date of Electronic Publication: 2025 Dec 03.

Publication Type: Journal Article

Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 100968539 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2261 (Electronic) Linking ISSN: 14712261 NLM ISO Abbreviation: BMC Cardiovasc Disord Subsets: MEDLINE

MeSH Terms: Coronary Artery Disease*/diagnostic imaging , Coronary Artery Disease*/physiopathology , Coronary Artery Disease*/mortality , Coronary Artery Disease*/therapy , Echocardiography, Three-Dimensional* , Atrial Function, Left* , Ventricular Function, Left* , Artificial Intelligence* , Image Interpretation, Computer-Assisted*, Humans ; Male ; Female ; Middle Aged ; Predictive Value of Tests ; Aged ; Prognosis ; Risk Assessment ; Time Factors ; Risk Factors ; Retrospective Studies ; Reproducibility of Results

Hybrid radiomic-HOG ensemble model for accurate pulmonary nodule diagnosis.

Authors: Krishnan O P J; Department of Computer Science and Engineering, National Institute of Technology Silchar, Cachar, 788010, Assam, India., Roy P; Department of Computer Science and Engineering, National Institute of Technology Silchar, Cachar, 788010, Assam, India.

Source: Biomedical physics & engineering express [Biomed Phys Eng Express] 2025 Dec 03; Vol. 12 (1). Date of Electronic Publication: 2025 Dec 03.

Publication Type: Journal Article

Journal Info: Publisher: IOP Publishing Ltd Country of Publication: England NLM ID: 101675002 Publication Model: Electronic Cited Medium: Internet ISSN: 2057-1976 (Electronic) Linking ISSN: 20571976 NLM ISO Abbreviation: Biomed Phys Eng Express Subsets: MEDLINE

MeSH Terms: Tomography, X-Ray Computed*/methods , Lung Neoplasms*/diagnostic imaging , Solitary Pulmonary Nodule*/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted*/methods , Image Processing, Computer-Assisted*/methods, Humans ; Support Vector Machine ; Algorithms ; ROC Curve ; Radiomics

HCViT-Net: Hybrid CNN and multi scale query transformer network for dermatological image segmentation.

Authors: Jiao W; Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China., Xu J; Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China., Fang Y; Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China., Huang J; Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China., Zhu Y; Department of Dermatology, Shanghai Ninth People's Hospital, Shanghai, China.; School of Medicine, Shanghai Jiaotong University, Shanghai, China., Ling D; Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China.

Source: Journal of applied clinical medical physics [J Appl Clin Med Phys] 2025 Dec; Vol. 26 (12), pp. e70385.

Publication Type: Journal Article

Journal Info: Publisher: Wiley on behalf of American Association of Physicists in Medicine Country of Publication: United States NLM ID: 101089176 Publication Model: Print Cited Medium: Internet ISSN: 1526-9914 (Electronic) Linking ISSN: 15269914 NLM ISO Abbreviation: J Appl Clin Med Phys Subsets: MEDLINE

MeSH Terms: Image Processing, Computer-Assisted*/methods , Neural Networks, Computer* , Dermoscopy*/methods , Skin Neoplasms*/diagnostic imaging , Image Interpretation, Computer-Assisted*/methods, Humans ; Algorithms

Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm.

Authors: Yasaka K; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan. koyasaka@gmail.com., Tsujimoto R; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan., Miyo R; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan., Abe O; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.

Source: Japanese journal of radiology [Jpn J Radiol] 2025 Dec; Vol. 43 (12), pp. 1993-2002. Date of Electronic Publication: 2025 Aug 09.

Publication Type: Journal Article

Journal Info: Publisher: Springer Country of Publication: Japan NLM ID: 101490689 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1867-108X (Electronic) Linking ISSN: 18671071 NLM ISO Abbreviation: Jpn J Radiol Subsets: MEDLINE

MeSH Terms: Artifacts* , Deep Learning* , Tomography, X-Ray Computed*/methods , Radiographic Image Interpretation, Computer-Assisted*/methods , Aorta*/diagnostic imaging, Humans ; Male ; Retrospective Studies ; Female ; Aged ; Algorithms ; Middle Aged ; Contrast Media ; Aged, 80 and over ; Motion

Artificial Intelligence Applications in the Diagnosis and Management of Cirrhosis and Portal Hypertension: A Narrative Review.

Authors: Khooblall A; Division of Vascular Imaging and Interventional Radiology, Imaging Institute, Cleveland Clinic Foundation, Cleveland, OH. Electronic address: khoobla@ccf.org., Viswanath SE; Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH; Louis Stokes Cleveland Veterans Affairs Medical Ctr, Cleveland, OH., Khawaja L; Northeast Ohio Medical University, Rootstown, OH., Gadani S; Division of Vascular Imaging and Interventional Radiology, Imaging Institute, Cleveland Clinic Foundation, Cleveland, OH; Division of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH.

Source: Techniques in vascular and interventional radiology [Tech Vasc Interv Radiol] 2025 Dec; Vol. 28 (4), pp. 101078. Date of Electronic Publication: 2025 Oct 01.

Publication Type: Journal Article; Review

Journal Info: Publisher: W.B. Saunders Country of Publication: United States NLM ID: 9806675 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1557-9808 (Electronic) Linking ISSN: 15579808 NLM ISO Abbreviation: Tech Vasc Interv Radiol Subsets: MEDLINE

MeSH Terms: Hypertension, Portal*/therapy , Hypertension, Portal*/diagnostic imaging , Hypertension, Portal*/physiopathology , Liver Cirrhosis*/therapy , Liver Cirrhosis*/diagnostic imaging , Liver Cirrhosis*/physiopathology , Artificial Intelligence* , Image Interpretation, Computer-Assisted* , Portal Pressure* , Radiography, Interventional*/adverse effects, Humans ; Predictive Value of Tests ; Portasystemic Shunt, Transjugular Intrahepatic/adverse effects ; Treatment Outcome ; Risk Factors

Effect of spatial resolution on the diagnostic performance of machine-learning radiomics model in lung adenocarcinoma: comparisons between normal- and high-spatial-resolution imaging for predicting invasiveness.

Authors: Yanagawa M; Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, The University of Osaka, 2-2 Yamadaoka, Suita-city, Osaka, 565-0871, Japan. m-yanagawa@radiol.med.osaka-u.ac.jp., Nagatani Y; Department of Radiology, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan., Hata A; Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, The University of Osaka, 2-2 Yamadaoka, Suita-city, Osaka, 565-0871, Japan., Sumikawa H; Department of Radiology, Kinki-Chuo Chest Medical Center, 1180 Nagasone-cho, Kita-ku, Sakai-City, 591-8555, Japan., Moriya H; Department of Radiology, Ohara General Hospital, 6-1 Uwamachi, Fukushima-city, Fukushima, 960-861, Japan., Iwano S; Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan., Tsuchiya N; Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, 207 Uehara, Nishihara-cho, Nakagami-gun, Okinawa, 903-0125, Japan., Iwasawa T; Department of Radiology, Kanagawa Cardiovascular & Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama, Kanagawa, 236-0051, Japan., Ohno Y; Department of Diagnostic Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan., Tomiyama N; Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, The University of Osaka, 2-2 Yamadaoka, Suita-city, Osaka, 565-0871, Japan.

Source: Japanese journal of radiology [Jpn J Radiol] 2025 Dec; Vol. 43 (12), pp. 2003-2017. Date of Electronic Publication: 2025 Jul 31.

Publication Type: Journal Article; Multicenter Study; Comparative Study

Journal Info: Publisher: Springer Country of Publication: Japan NLM ID: 101490689 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1867-108X (Electronic) Linking ISSN: 18671071 NLM ISO Abbreviation: Jpn J Radiol Subsets: MEDLINE

MeSH Terms: Machine Learning* , Tomography, X-Ray Computed*/methods , Lung Neoplasms*/diagnostic imaging , Lung Neoplasms*/pathology , Adenocarcinoma of Lung*/diagnostic imaging , Adenocarcinoma of Lung*/pathology , Radiographic Image Interpretation, Computer-Assisted*/methods, Humans ; Retrospective Studies ; Male ; Female ; Middle Aged ; Aged ; Neoplasm Invasiveness/diagnostic imaging ; Sensitivity and Specificity ; Lung/diagnostic imaging ; Lung/pathology ; Aged, 80 and over ; Radiomics

Improved Breast Cancer Detection with Artificial Intelligence in a Real-World Digital Breast Tomosynthesis Screening Program.

Authors: Nepute JA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indiana University Health - West Central Region, Avon, IN., Peratikos M; Biostatistics Consulting, LLC, Kensington, MD., Toledano AY; Biostatistics Consulting, LLC, Kensington, MD., Salvas JP; Indiana University School of Medicine, Indianapolis, IN., Delks H; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indiana University Health - West Central Region, Avon, IN., Shisler JL; JLS Consulting, Jupiter, FL., Hoffmeister JW; iCAD, Inc, Nashua, NH., Madden CM; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IUH Ball Memorial Hospital, Muncie, IN. Electronic address: comadden@iu.edu.

Source: Clinical breast cancer [Clin Breast Cancer] 2025 Dec; Vol. 25 (8), pp. 808-816.e5. Date of Electronic Publication: 2025 May 09.

Publication Type: Journal Article

Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 100898731 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1938-0666 (Electronic) Linking ISSN: 15268209 NLM ISO Abbreviation: Clin Breast Cancer Subsets: MEDLINE

MeSH Terms: Breast Neoplasms*/diagnosis , Breast Neoplasms*/diagnostic imaging , Mammography*/methods , Mammography*/statistics & numerical data , Early Detection of Cancer*/methods , Early Detection of Cancer*/statistics & numerical data , Artificial Intelligence* , Radiographic Image Interpretation, Computer-Assisted*/methods, Humans ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Deep Learning ; Breast/diagnostic imaging ; Breast/pathology

Hard exudates segmentation for retinal fundus images based on longitudinal multi-scale fusion network.

Authors: Liu S; School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China., Jiang X; School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China., Zhang J; School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China., Zou W; School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China. zouwei@suda.edu.cn.

Source: Medical & biological engineering & computing [Med Biol Eng Comput] 2025 Dec; Vol. 63 (12), pp. 3761-3775. Date of Electronic Publication: 2025 Aug 13.

Publication Type: Journal Article

Journal Info: Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE

MeSH Terms: Fundus Oculi* , Exudates and Transudates*/diagnostic imaging , Retina*/diagnostic imaging , Image Processing, Computer-Assisted*/methods , Retinal Diseases*/diagnostic imaging , Image Interpretation, Computer-Assisted*/methods, Humans ; Algorithms ; Neural Networks, Computer

DIM: A diffusion instability measure for MRI quality assurance.

Authors: Schmidt T; Laboratory for Social and Neural Systems Research (SNS Lab), University of Zurich, Zurich, Switzerland.; Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland., Nagy Z; Laboratory for Social and Neural Systems Research (SNS Lab), University of Zurich, Zurich, Switzerland.

Source: Magnetic resonance in medicine [Magn Reson Med] 2025 Dec; Vol. 94 (6), pp. 2624-2631. Date of Electronic Publication: 2025 Aug 10.

Publication Type: Journal Article

Journal Info: Publisher: Wiley Country of Publication: United States NLM ID: 8505245 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-2594 (Electronic) Linking ISSN: 07403194 NLM ISO Abbreviation: Magn Reson Med Subsets: MEDLINE

MeSH Terms: Diffusion Magnetic Resonance Imaging*/methods , Diffusion Magnetic Resonance Imaging*/standards , Diffusion Magnetic Resonance Imaging*/instrumentation , Quality Assurance, Health Care*/standards , Quality Assurance, Health Care*/methods , Image Interpretation, Computer-Assisted*/methods , Artifacts*, Phantoms, Imaging ; Reproducibility of Results ; Sensitivity and Specificity ; Algorithms ; Humans

A Preliminary Study on an Intelligent Segmentation and Classification Model for Amygdala-Hippocampus MRI Images in Alzheimer's Disease.

Authors: Liu S; Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai 200040, China (S.L., D.G.)., Zhou K; Academy for Engineering and Technology, Fudan University, Shanghai 200032, China (K.Z.)., Geng D; Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai 200040, China (S.L., D.G.); Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases, Shanghai 200031, China (D.G.); Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai 200032, China (D.G.). Electronic address: gengdy@163.com.

Source: Academic radiology [Acad Radiol] 2025 Dec; Vol. 32 (12), pp. 7459-7470. Date of Electronic Publication: 2025 Sep 02.

Publication Type: Journal Article; Multicenter Study

Journal Info: Publisher: Association Of University Radiologists Country of Publication: United States NLM ID: 9440159 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-4046 (Electronic) Linking ISSN: 10766332 NLM ISO Abbreviation: Acad Radiol Subsets: MEDLINE

MeSH Terms: Alzheimer Disease*/pathology , Alzheimer Disease*/diagnostic imaging , Amygdala*/pathology , Amygdala*/diagnostic imaging , Magnetic Resonance Imaging*/methods , Deep Learning* , Hippocampus*/pathology , Hippocampus*/diagnostic imaging , Image Interpretation, Computer-Assisted*/methods, Humans ; Male ; Female ; Aged ; China ; Middle Aged ; Reproducibility of Results

Artificial intelligence in echocardiography: current applications and future perspectives.

Authors: Sakamoto A; Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan., Kaneko T; Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan., Sato E; Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan., Fujita W; Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan., Nakamura Y; Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan., Yokotsuka N; Clinical Laboratory, Juntendo University Hospital, Tokyo, Japan., Kagiyama N; Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan. kgnb_27_hot@yahoo.co.jp.; Data Science Course, Juntendo University, Tokyo, Japan. kgnb_27_hot@yahoo.co.jp.

Source: Journal of echocardiography [J Echocardiogr] 2025 Dec; Vol. 23 (4), pp. 231-240. Date of Electronic Publication: 2025 Aug 21.

Publication Type: Journal Article; Review

Journal Info: Publisher: Springer Japan Country of Publication: Japan NLM ID: 101263153 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1880-344X (Electronic) Linking ISSN: 13490222 NLM ISO Abbreviation: J Echocardiogr Subsets: MEDLINE

MeSH Terms: Artificial Intelligence*/trends , Echocardiography*/methods , Echocardiography*/trends , Image Interpretation, Computer-Assisted*/methods, Humans ; Deep Learning

Applications of artificial intelligence in abdominal imaging.

Authors: Gupta A; All India Institute of Medical Sciences, New Delhi, India., Rajamohan N; The University of Texas Southwestern Medical Center, Dallas, United States., Bansal B; The University of Texas Southwestern Medical Center, Dallas, United States., Chaudhri S; Jawaharlal Institute of Post Graduate Medical Education and Research, Puducherry, India., Chandarana H; Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, United States., Bagga B; NYU Grossman School of Medicine, New York, United States. Barun.Bagga@nyulangone.org.; NYU Langone Hospital - Long Island, Mineola, United States. Barun.Bagga@nyulangone.org.

Source: Abdominal radiology (New York) [Abdom Radiol (NY)] 2025 Dec; Vol. 50 (12), pp. 6172-6191. Date of Electronic Publication: 2025 May 26.

Publication Type: Journal Article; Review

Journal Info: Publisher: Springer Country of Publication: United States NLM ID: 101674571 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2366-0058 (Electronic) NLM ISO Abbreviation: Abdom Radiol (NY) Subsets: MEDLINE

MeSH Terms: Artificial Intelligence* , Abdomen*/diagnostic imaging , Image Interpretation, Computer-Assisted*/methods, Humans

Contrastive learning and prior knowledge-induced feature extraction network for prediction of high-risk recurrence areas in Gliomas.

Authors: Wu B; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Ward 1 of the Radiotherapy Department, The First Affiliated Hospital of Hainan Medical University, Haikou 570102, P.R. China., Cao J; Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China., Xiong W; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China., Lv Y; Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China., Zhao G; Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China; Tianjian Laboratory of Advanced Biomedical Sciences, Zhengzhou 450007, China., Ma X; Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China; Tianjian Laboratory of Advanced Biomedical Sciences, Zhengzhou 450007, China., Zhang Y; Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China., Zhang J; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China., Bu J; Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China., Xie T; Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China. Electronic address: jessie0860@sina.cn., Feng Q; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China. Electronic address: fengqj99@smu.edu.cn., Huang M; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China. Electronic address: huangmeiyan88@smu.edu.cn.

Source: Medical image analysis [Med Image Anal] 2025 Dec; Vol. 106, pp. 103740. Date of Electronic Publication: 2025 Aug 05.

Publication Type: Journal Article

Journal Info: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 9713490 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1361-8423 (Electronic) Linking ISSN: 13618415 NLM ISO Abbreviation: Med Image Anal Subsets: MEDLINE

MeSH Terms: Glioma*/diagnostic imaging , Glioma*/pathology , Neoplasm Recurrence, Local*/diagnostic imaging , Brain Neoplasms*/diagnostic imaging , Brain Neoplasms*/pathology , Magnetic Resonance Imaging*/methods , Image Interpretation, Computer-Assisted*/methods , Machine Learning*, Humans

Mixture-attention Siamese transformer for video polyp segmentation.

Authors: Chen G; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China., Yang J; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China., Pu X; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China., Ji GP; School of Computing, Australian National University, Canberra, Australia., Xiong H; Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates; Harbin Institute of Technology, China., Pan Y; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China., Cui H; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China., Xia Y; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China. Electronic address: yxia@nwpu.edu.cn.

Source: Artificial intelligence in medicine [Artif Intell Med] 2025 Dec; Vol. 170, pp. 103278. Date of Electronic Publication: 2025 Oct 03.

Publication Type: Journal Article

Journal Info: Publisher: Elsevier Science Publishing Country of Publication: Netherlands NLM ID: 8915031 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2860 (Electronic) Linking ISSN: 09333657 NLM ISO Abbreviation: Artif Intell Med Subsets: MEDLINE

MeSH Terms: Colonoscopy*/methods , Colonic Polyps*/diagnostic imaging , Colonic Polyps*/pathology , Colonic Polyps*/diagnosis , Video Recording* , Image Interpretation, Computer-Assisted*/methods , Image Processing, Computer-Assisted*/methods, Humans ; Algorithms

Research-based clinical deployment of artificial intelligence algorithm for prostate MRI.

Authors: Harmon SA; National Institutes of Health, Bethesda, USA. stephanie.harmon@nih.gov., Tetreault J; Nvidia (United States), Santa Clara, USA., Esengur OT; National Institutes of Health, Bethesda, USA., Qin M; Nvidia (United States), Santa Clara, USA., Yilmaz EC; National Institutes of Health, Bethesda, USA., Chang V; Nvidia (United States), Santa Clara, USA., Yang D; Nvidia (United States), Santa Clara, USA., Xu Z; Nvidia (United States), Santa Clara, USA., Cohen G; National Institutes of Health, Bethesda, USA., Plum J; National Institutes of Health, Bethesda, USA., Sherif T; National Institutes of Health, Bethesda, USA., Levin R; National Institutes of Health, Bethesda, USA., Schmidt-Richberg A; Philips (United States), Andover, USA., Thompson S; Philips (United States), Andover, USA., Coons S; Philips (United States), Andover, USA., Chen T; National Institutes of Health, Bethesda, USA., Choyke PL; National Institutes of Health, Bethesda, USA., Xu D; Nvidia (United States), Santa Clara, USA., Gurram S; National Institutes of Health, Bethesda, USA., Wood BJ; National Institutes of Health, Bethesda, USA., Pinto PA; National Institutes of Health, Bethesda, USA., Turkbey B; National Institutes of Health, Bethesda, USA. turkbeyi@mail.nih.gov.

Source: Abdominal radiology (New York) [Abdom Radiol (NY)] 2025 Dec; Vol. 50 (12), pp. 5893-5902. Date of Electronic Publication: 2025 May 26.

Publication Type: Journal Article

Journal Info: Publisher: Springer Country of Publication: United States NLM ID: 101674571 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2366-0058 (Electronic) NLM ISO Abbreviation: Abdom Radiol (NY) Subsets: MEDLINE

MeSH Terms: Artificial Intelligence* , Prostatic Neoplasms*/diagnostic imaging , Prostatic Neoplasms*/pathology , Algorithms* , Magnetic Resonance Imaging*/methods , Image Interpretation, Computer-Assisted*/methods , Multiparametric Magnetic Resonance Imaging*/methods, Humans ; Male ; Radiology Information Systems ; Prospective Studies ; Prostate/diagnostic imaging

Multi-class deep learning architecture for COVID-19, tuberculosis, and pneumonia classification using chest X-ray images.

Authors: Srivastava S; School of Computing Science Engineering and Artificial Intelligence, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, 466114, Madhya Pradesh, India., Ghosh E; School of Computing Science Engineering and Artificial Intelligence, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, 466114, Madhya Pradesh, India., Kumar A; School of Computing Science Engineering and Artificial Intelligence, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, 466114, Madhya Pradesh, India., Chahar P; School of Computing Science Engineering and Artificial Intelligence, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, 466114, Madhya Pradesh, India., Utkarsh A; School of Computing Science Engineering and Artificial Intelligence, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, 466114, Madhya Pradesh, India., Mishra R; School of Computing Science Engineering and Artificial Intelligence, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, 466114, Madhya Pradesh, India. Electronic address: raghavendra.mishra@vitbhopal.ac.in.

Source: Journal of medical imaging and radiation sciences [J Med Imaging Radiat Sci] 2025 Dec; Vol. 56 (6), pp. 102115. Date of Electronic Publication: 2025 Oct 08.

Publication Type: Journal Article

Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 101469694 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1876-7982 (Electronic) Linking ISSN: 18767982 NLM ISO Abbreviation: J Med Imaging Radiat Sci Subsets: MEDLINE

MeSH Terms: Deep Learning* , COVID-19*/diagnostic imaging , Radiography, Thoracic*/methods , Pneumonia*/diagnostic imaging , Tuberculosis*/diagnostic imaging , Tuberculosis, Pulmonary*/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted*/methods, Humans ; Neural Networks, Computer ; SARS-CoV-2 ; Lung/diagnostic imaging