Use of artificial intelligence in imaging in rheumatology – current status and future perspectives

Stoel, Berend C.

doi:10.1136/rmdopen-2019-001063

Cited by 64 publications

(55 citation statements)

References 48 publications

(58 reference statements)

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“…CNN is utilized mostly in imaging undertakings [21]. e authors of [22] report the successful utilization of CNN to advance computerized scoring of the activity of joint inflammation infection on ultrasound images.…”

Section: Related Workmentioning

confidence: 99%

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Mate

Kureshi²,

Singh

2021

Journal of Healthcare Engineering

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Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.

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Section: Related Workmentioning

confidence: 99%

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Mate

Kureshi²,

Singh

2021

Journal of Healthcare Engineering

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“…Ophthalmic imaging, e.g., fundus digital photography, optical coherence tomography, among other imaging fields, is where artificial intelligence can support the specialist in the diagnosis of ophthalmic disorders, such as diabetic retinopathy, age-related macular degeneration, and others [65]. Other areas include cardiology [66,67] and rheumatology, which have a long history of research in AI applications aimed to detect and assess also rheumatological manifestations, bone erosions, and cartilage loss [68]. The development of digital pathology, due to the introduction of whole-slide scanners, and the progression of computer vision algorithms have significantly grown the usage of AI to perform tumor diagnosis, subtyping, grading, staging, and prognostic prediction.…”

Section: Imagingmentioning

confidence: 99%

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

et al. 2021

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: Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.

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“…Imaging is one of the fields that benefit the most from the advances of AI methods. Indeed, machine learning methods such as artificial neural networks enable an automatic analysis of images, with different levels of interpretation of the findings (fully human, semi-automatic interpretation or fully automatic interpretation) [ 15 ]. In rheumatoid arthritis (RA), such methods have been applied to identify and quantify synovitis or tenosynovitis on MRI or ultrasonography [ 16 ], and to detect bone erosions on hand radiographs [ 17 ].…”

Section: Other Examples Of Ai In Rheumatologymentioning

confidence: 99%

Wearable activity trackers and artificial intelligence in the management of rheumatic diseases

2021

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Wearable activity trackers are playing an increasingly important role in healthcare. In the field of rheumatic and musculoskeletal diseases (RMDs), various applications are currently possible. This review will present the use of activity trackers to promote physical activity levels in rheumatology, as well as the use of trackers to measure health parameters and detect flares using artificial intelligence. Challenges and limitations of the use of artificial intelligence will be discussed, as well as technical issues when using activity trackers in clinical practice.

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Use of artificial intelligence in imaging in rheumatology – current status and future perspectives

Cited by 64 publications

References 48 publications

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

Wearable activity trackers and artificial intelligence in the management of rheumatic diseases

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