Physician centred imaging interpretation is dying out — why should I be a nuclear medicine physician?

Hustinx, Roland

doi:10.1007/s00259-019-04371-y

Cited by 14 publications

(16 citation statements)

References 48 publications

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“…Overall, there is growing emphasis on using machine learning to drive personalized interventions along the care continuum (Volpp and Mohta 2016 ), but the future is still uncertain and there is not a definite answer to the question “Will be imagers outgunned by “intelligent” algorithms?” ( https://www.radiologybusiness.com/topics/artificial-intelligence/wait-will-ai-replace-radiologists-after-all 2020 ). Notwithstanding this question has been outdated by the more politically correct motto that “imagers using AI will replace those who don’t” (Hustinx 2019 ), ungraduated medical students are probably more aware on AI medical potentiality and business opportunities, than nowadays imagers rather afraid of turf losses (Pinto Dos Santos et al 2019 ; van Hoek et al 2019 ). What we—as physicians and academia—should be familiar with is that to incorporate AI into daily practice, it is necessary to provide users a basic knowledge in artificial intelligence, well understanding both its pros and cons.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Sollini

Bartoli

Marciano

et al. 2020

European J Hybrid Imaging

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Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer—being one of the most extensively malignancy studied by hybrid medical imaging—has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Sollini

Bartoli

Marciano

et al. 2020

European J Hybrid Imaging

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“…The capability of automated diagnosis or prediction of rare/unknown outcomes has been closely linked to the superhuman performance of AI. Micro-metastases (early metastatic disease) detection, prediction of survival or response to therapy, and in general identification of complex cases and/or rare diseases are among the major applications of AI in nuclear medicine (Ellmann et al 2019 ; Hustinx 2019 ). Figure 1 illustrates two examples of the utilization of deep learning methods in molecular imaging in supervised and unsupervised learning.…”

Section: Principles Of Machine Learning and Deep Learningmentioning

confidence: 99%

“…AI approaches and in particular deep learning methods have witnessed impressive progress over the past few years showing great promise for future applications in molecular imaging. Though AI methods still have a long way to go to play a major role in clinical practice and undertake part of the radiologists’ responsibilities (Hustinx 2019 ; Mazurowski 2019 ; Yi et al 2018 ), it is time to define and introduce the frameworks, protocols, and standards to exploit these approaches as an alternative option or to assist processes and decisions taken in clinical practice. The performance of AI approaches could equal or even surpass human/specialist’s performance in a variety of applications in medicine.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy

Arabi

Zaidi

2020

European J Hybrid Imaging

126

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This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and radiation therapy research. To this end, the applications of artificial intelligence in five generic fields of molecular imaging and radiation therapy, including PET instrumentation design, PET image reconstruction quantification and segmentation, image denoising (low-dose imaging), radiation dosimetry and computer-aided diagnosis, and outcome prediction are discussed. This review sets out to cover briefly the fundamental concepts of AI and deep learning followed by a presentation of seminal achievements and the challenges facing their adoption in clinical setting.

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“…During the diagnosis process, radiologist need to read a large number of image data and ML has made great progress in the reconstruction of CT, SPECT and PET (101,102). At the same time, ML is very helpful in assisting radiologists to diagnose and treat diseases (103)(104)(105).…”

Section: Section 3: Application Of ML In Nuclear Medical Imagingmentioning

confidence: 99%

“…The combination of ML and imaging has been widely used in the detection and diagnosis of tumors (103). The diagnosis of lung nodules is an important part of the daily work of radiologists and how to diagnose the lung nodules is a tedious and complicated process.…”

Section: Application Of ML In Petmentioning

confidence: 99%

A review of the application of machine learning in molecular imaging

Yin¹,

Cao²,

Wang³

et al. 2021

Ann Transl Med

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Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Optical molecular imaging (OMI) and nuclear medical imaging are two key research fields of MI.OMI technology refers to the optical information generated by the imaging target (such as tumors) due to drug intervention and other reasons. By collecting the optical information, researchers can track the motion trajectory of the imaging target at the molecular level. Owing to its high specificity and sensitivity, OMI has been widely used in preclinical research and clinical surgery. Nuclear medical imaging mainly detects ionizing radiation emitted by radioactive substances. It can provide molecular information for early diagnosis, effective treatment and basic research of diseases, which has become one of the frontiers and hot topics in the field of medicine in the world today. Both OMI and nuclear medical imaging technology require a lot of data processing and analysis. In recent years, artificial intelligence technology, especially neural networkbased machine learning (ML) technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of ML methods in OMI and nuclear medical imaging.

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Physician centred imaging interpretation is dying out — why should I be a nuclear medicine physician?

Cited by 14 publications

References 48 publications

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy

A review of the application of machine learning in molecular imaging

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