MRI-based artificial intelligence to predict infection following total hip arthroplasty failure

Albano, Domenico; Gitto, Salvatore; Messina, Carmelo; Serpi, Francesca; Salvatore, Christian; Castiglioni, Isabella; Zagra, Luigi; Vecchi, Elena De; Sconfienza, Luca Maria

doi:10.1007/s11547-023-01608-7

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…Further research is also needed to compare AI alone and as an adjunct with human experts in evaluating orthopedic implants [ 99 ].…”

Section: Orthopedic Implants and Implant-related Complicationmentioning

confidence: 99%

AI applications in musculoskeletal imaging: a narrative review

Gitto,

Serpi,

Albano

et al. 2024

Eur Radiol Exp

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This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice.Key points• AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance. Graphical Abstract

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“…Further research is also needed to compare AI alone and as an adjunct with human experts in evaluating orthopedic implants [ 99 ].…”

Section: Orthopedic Implants and Implant-related Complicationmentioning

confidence: 99%

AI applications in musculoskeletal imaging: a narrative review

Gitto,

Serpi,

Albano

et al. 2024

Eur Radiol Exp

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“…Conventional CT remains inadequate to identify BME, which has always been exclusive prerogative of MRI [ 53 – 58 ]. VNCa DECT algorithm allows to eliminate the contribution of calcium to attenuation values of the bone [ 59 , 60 ].…”

Section: Musculoskeletal Applicationsmentioning

confidence: 99%

Dual-energy CT in musculoskeletal imaging: technical considerations and clinical applications

Albano,

Di Luca,

D’Angelo

et al. 2024

Radiol med

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Dual-energy CT stands out as a robust and innovative imaging modality, which has shown impressive advancements and increasing applications in musculoskeletal imaging. It allows to obtain detailed images with novel insights that were once the exclusive prerogative of magnetic resonance imaging. Attenuation data obtained by using different energy spectra enable to provide unique information about tissue characterization in addition to the well-established strengths of CT in the evaluation of bony structures. To understand clearly the potential of this imaging modality, radiologists must be aware of the technical complexity of this imaging tool, the different ways to acquire images and the several algorithms that can be applied in daily clinical practice and for research. Concerning musculoskeletal imaging, dual-energy CT has gained more and more space for evaluating crystal arthropathy, bone marrow edema, and soft tissue structures, including tendons and ligaments. This article aims to analyze and discuss the role of dual-energy CT in musculoskeletal imaging, exploring technical aspects, applications and clinical implications and possible perspectives of this technique.

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“…Artificial intelligence (AI) is a discipline in computer science concerned with creating ‘machines’ that can mimic the cognitive capabilities of human intelligence [ 4 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review

Albano,

Galiano,

Basile

et al. 2024

BMC Oral Health

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Background The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). Materials and methods An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. Results Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3–0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. Conclusion AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. Protocol PROSPERO identifier: CRD42023470708

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MRI-based artificial intelligence to predict infection following total hip arthroplasty failure

Cited by 9 publications

References 27 publications

AI applications in musculoskeletal imaging: a narrative review

AI applications in musculoskeletal imaging: a narrative review

Dual-energy CT in musculoskeletal imaging: technical considerations and clinical applications

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review

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