Shear-wave elastography for the evaluation of tendinopathies: a systematic review and meta-analysis.

PURPOSE: To compare pathologic and healthy tendons using shear-wave elastography (SWE). METHODS: A systematic review with meta-analysis was done searching Pubmed and EMBASE up to September 2022. Prospective, retrospective and cross-sectional studies that used SWE in the assessment of pathologic tendons versus control were included. Our primary outcome were SWE velocity (m/s) and stiffness (kPa). Methodological quality was assessed by the methodological index for non-randomized studies (MINORS). We used the mean difference (MD) with corresponding 95% confidence intervals (CIs) to quantify effects between groups. We performed sensitivity analysis in case of high heterogeneity, after excluding poor quality studies according to MINORS assessment. We used Grades of Recommendation, Assessment, Development and Evaluation to evaluate the certainty of evidence (CoE). RESULTS: Overall, 16 studies with 676 pathologic tendons (188 Achilles, 142 patellar, 96 supraspinatus, 250 mixed) and 723 control tendons (484 healthy; 239 contralateral tendon) were included. Five studies (31.3%) were judged as poor methodological quality. Shear-wave velocity and stiffness meta-analyses showed high heterogeneity. According to a sensitivity analysis, pathologic tendons had a lower shear wave velocity (MD of - 1.69 m/s; 95% CI 1.85; - 1.52; n = 274; I2 50%) compared to healthy tendons with very low CoE. Sensitivity analysis on stiffness still showed high heterogeneity. CONCLUSION: Pathological tendons may have reduced SWE velocity compared to controls, but the evidence is very uncertain. Future robust high-quality longitudinal studies and clear technical indications on the use of this tool are needed. PROTOCOL: PROSPERO identifier: CRD42023405410 CLINICAL RELEVANCE STATEMENT: SWE is a relatively recent modality that may increase sensitivity and diagnostic accuracy of conventional ultrasound imaging promoting early detection of tendinopathy. Non-negligible heterogeneity has been observed in included studies, so our findings may encourage the conduct of future high-quality longitudinal studies which can provide clear technical indications on the use of this promising tool in tendon imaging.

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

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.

Shear-wave elastography of the plantar fascia: a systematic review and meta-analysis.

PURPOSE: To assess the efficacy of shear-wave elastography (SWE) of the plantar fascia (PF) in identifying plantar fasciitis. METHODS: A literature search was conducted on the PubMed and Medline databases for articles published up to August 2022. The Newcastle-Ottawa scale was used to assess the risk of bias. We included original research studies in English dealing with the evaluation of patients with plantar fasciitis by means of SWE and including shear modulus (KPa) and/or shear-wave velocity (m/s). We compared healthy and pathologic PF stiffness using the standardised mean difference (SMD) in a random-effects model (95% CI). RESULTS: Five studies were included with a total of 158 pathologic PFs and 134 healthy PFs. No significant publication bias was detected. Studies were highly heterogeneous (p < 0.00001; I2 = 97%). Pathologic PFs showed significantly lower stiffness, with an SMD of - 3.00 m/s (95% confidence interval: - 4.95 to - 1.06, p = 0.002), compared to healthy PF. CONCLUSION: Pathologic PFs present significantly lower stiffness than healthy PFs. However, the analysed studies are highly heterogeneous.

Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review.

The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.