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Comment
. 2024 Nov;6(6):e240670.
doi: 10.1148/ryai.240670.

Achieving More with Less: Combining Strong and Weak Labels for Intracranial Hemorrhage Detection

Tugba Akinci D'Antonoli  1 , Jeffrey D Rudie  1
Affiliations
Comment

Achieving More with Less: Combining Strong and Weak Labels for Intracranial Hemorrhage Detection

Tugba Akinci D'Antonoli et al. Radiol Artif Intell. 2024 Nov.
No abstract available

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Conflict of interest statement

Disclosures of conflicts of interest: T.A.D. Support for attending meetings and/or travel from KSBL and the European Society of Medical Imaging Informatics (EuSoMII); member of the Radiology: Artificial Intelligence trainee editorial board; member of the European Society of Radiology scientific editorial board for European Radiology; member of the Turkish Society of Radiology editorial board for Diagnostic and Interventional Radiology; member of the EuSoMII Young Club Committee and Scientific Community. J.D.R. Consulting fees from Cortechs.ai; associate editor for Radiology: Artificial Intelligence; Radiological Society of North America AI Committee member; member of the advisory panel for the Radiology: Artificial Intelligence trainee editorial board; Radiology: Artificial Intelligence trainee editorial board alum; member of medical advisory board at Cortechs.ai and Subtle Medical; stock or stock options in Cortechs.ai and Subtle Medical.

Figures

Tugba Akinci D’Antonoli, MD, is currently a radiology resident at Cantonal Hospital Baselland and a researcher at the University of Basel, Switzerland. Her research interests include deep learning and radiomics applications in cardiothoracic radiology and neuroradiology. She is a member of the 2023–2025 trainee editorial board for Radiology: Artificial Intelligence and is also a member of the Young Club Committee in the European Society of Medical Imaging Informatics and a scientific editorial board member for European Radiology and Diagnostic and Interventional Radiology.
Tugba Akinci D’Antonoli, MD, is currently a radiology resident at Cantonal Hospital Baselland and a researcher at the University of Basel, Switzerland. Her research interests include deep learning and radiomics applications in cardiothoracic radiology and neuroradiology. She is a member of the 2023–2025 trainee editorial board for Radiology: Artificial Intelligence and is also a member of the Young Club Committee in the European Society of Medical Imaging Informatics and a scientific editorial board member for European Radiology and Diagnostic and Interventional Radiology.
Jeffrey D. Rudie, MD, PhD, is a neuroradiologist at Scripps Clinic Medical Group in San Diego, as well as an adjunct assistant professor of radiology at University of California San Diego. He is an associate editor for Radiology: Artificial Intelligence and a member of the RSNA AI committee. His research is focused on developing and translating AI into clinical practice to improve the accuracy and efficiency of neuroradiology, including using deep learning–based segmentation methods for longitudinal assessment in neuro-oncology. He is the lead organizer of the 2024 International Brain Tumor Segmentation Challenge (BraTS) on adult posttreatment glioma and the 2025 RSNA AI challenge on intracranial aneurysm detection.
Jeffrey D. Rudie, MD, PhD, is a neuroradiologist at Scripps Clinic Medical Group in San Diego, as well as an adjunct assistant professor of radiology at University of California San Diego. He is an associate editor for Radiology: Artificial Intelligence and a member of the RSNA AI committee. His research is focused on developing and translating AI into clinical practice to improve the accuracy and efficiency of neuroradiology, including using deep learning–based segmentation methods for longitudinal assessment in neuro-oncology. He is the lead organizer of the 2024 International Brain Tumor Segmentation Challenge (BraTS) on adult posttreatment glioma and the 2025 RSNA AI challenge on intracranial aneurysm detection.
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References

    1. GBD 2019 Stroke Collaborators . Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019 . Lancet Neurol 2021. ; 20 ( 10 ): 795 – 820 . - PMC - PubMed
    1. An SJ , Kim TJ , Yoon BW . Epidemiology, risk factors, and clinical features of intracerebral hemorrhage: an update . J Stroke 2017. ; 19 ( 1 ): 3 – 10 . - PMC - PubMed
    1. Kalidindi S , Gandhi S . Workforce crisis in radiology in the UK and the strategies to deal with it: is artificial intelligence the saviour? Cureus 2023. ; 15 ( 8 ): e43866 . - PMC - PubMed
    1. Seyam M , Weikert T , Sauter A , Brehm A , Psychogios M-N , Blackham KA . Utilization of artificial intelligence-based intracranial hemorrhage detection on emergent noncontrast CT images in clinical workflow . Radiol Artif Intell 2022. ; 4 ( 2 ): e210168 . - PMC - PubMed
    1. Yun TJ , Choi JW , Han M , et al. . Deep learning based automatic detection algorithm for acute intracranial haemorrhage: a pivotal randomized clinical trial . NPJ Digit Med 2023. ; 6 ( 1 ): 61 . - PMC - PubMed

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