Measuring appropriateness of diagnostic imaging: a scoping review

Walther, Felix; Eberlein-Gonska, Maria; Hoffmann, Ralf‐Thorsten; Schmitt, Jochen; Blum, Sophia

doi:10.1186/s13244-023-01409-6

Cited by 5 publications

(1 citation statement)

References 108 publications

(264 reference statements)

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“…Xenobiotics, like various classes of drugs, cosmetics, pesticides, food additives, industrial chemicals, environmental pollutants, etc., are some common chemicals that individuals can get exposed to in their lifetime (Patterson et al 2010 ). Among these etiological factors, xenobiotics such as pesticides and ionizing radiation (IR) are at the forefront, mainly due to their increasing application (Walther et al 2023 ). Pesticides have been linked to neurodegeneration through oxidative stress, mitochondrial dysfunction, DNA damage and altered cellular signalling (Narasimhamurthy et al 2022a ; Rodrigues et al 2022 ; Prathiksha et al 2023 ), while IR has also been studied for their role in inducing neurotoxicity (Sharma et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Low-dose exposure to malathion and radiation results in the dysregulation of multiple neuronal processes, inducing neurotoxicity and neurodegeneration in mouse

Narasimhamurthy,

Venkidesh,

Nayak

et al. 2023

Environ Sci Pollut Res

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Neurodegenerative disorders are a debilitating and persistent threat to the global elderly population, carrying grim outcomes. Their genesis is often multifactorial, with a history of prior exposure to xenobiotics such as pesticides, heavy metals, enviornmental pollutants, ionizing radiation etc,. A holistic molecular insight into their mechanistic induction upon single or combinatorial exposure to different toxicants is still unclear. In the present study, one-month-old C57BL/6 male mice were administered orally with malathion (50 mg/kg body wt. for 14 days) and single whole-body radiation (0.5 Gy) on the 8th day. Post-treatment, behavioural assays for exploratory behaviour, memory, and learning were performed. After sacrifice, brains were collected for histology, biochemical assays, and transcriptomic analysis. Transcriptomic analysis revealed several altered processes like synaptic transmission and plasticity, neuronal survival, proliferation, and death. Signalling pathways like MAPK, PI3K-Akt, Apelin, NF-κB, cAMP, Notch etc., and pathways related to neurodegenerative diseases were altered. Increased astrogliosis was observed in the radiation and coexposure groups, with significant neuronal cell death and a reduction in the expression of NeuN. Sholl analysis, dendritic arborization and spine density studies revealed decreased total apical neuronal path length and dendritic spine density. Reduced levels of the antioxidants GST and GSH and acetylcholinesterase enzyme activity were also detected. However, no changes were seen in exploratory behaviour or learning and memory post-treatment. Thus, explicating the molecular mechanisms behind malathion and radiation can provide novel insights into external factor-driven neurotoxicity and neurodegenerative pathogenesis.

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Section: Introductionmentioning

confidence: 99%

Low-dose exposure to malathion and radiation results in the dysregulation of multiple neuronal processes, inducing neurotoxicity and neurodegeneration in mouse

Narasimhamurthy,

Venkidesh,

Nayak

et al. 2023

Environ Sci Pollut Res

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Machine learning and deep learning for classifying the justification of brain CT referrals

Potočnik,

Thomas,

Lawlor

et al. 2024

Eur Radiol

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Objectives To train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts. Methods Adult brain computed tomography (CT) referrals from scans performed in three CT centres in Ireland in 2020 and 2021 were retrospectively collected. Two radiographers analysed the justification of 3000 randomly selected referrals using iGuide, with two consultant radiologists analysing the referrals with disagreement. Insufficient or duplicate referrals were discarded. The inter-rater agreement among radiographers and consultants was computed. A random split (4:1) was performed to apply machine learning (ML) and deep learning (DL) techniques to unstructured clinical indications to automate retrospective justification auditing with multi-class classification. The accuracy and macro-averaged F1 score of the best-performing classifier of each type on the training set were computed on the test set. Results 42 referrals were ignored. 1909 (64.5%) referrals were justified, 811 (27.4%) were potentially justified, and 238 (8.1%) were unjustified. The agreement between radiographers (κ = 0.268) was lower than radiologists (κ = 0.460). The best-performing ML model was the bag-of-words-based gradient-boosting classifier achieving a 94.4% accuracy and a macro F1 of 0.94. DL models were inferior, with bi-directional long short-term memory achieving 92.3% accuracy, a macro F1 of 0.92, and outperforming multilayer perceptrons. Conclusion Interpreting unstructured clinical indications is challenging necessitating clinical decision support. ML and DL can generalise across multiple clinical sites, outperform human experts, and be used as an artificial intelligence-based iGuide interpreter when retrospectively vetting radiology referrals. Clinical relevance statement Healthcare vendors and clinical sites should consider developing and utilising artificial intelligence-enabled systems for justifying medical exposures. This would enable better implementation of imaging referral guidelines in clinical practices and reduce population dose burden, CT waiting lists, and wasteful use of resources. Key Points Significant variations exist among human experts in interpreting unstructured clinical indications/patient presentations. Machine and deep learning can automate the justification analysis of radiology referrals according to iGuide categorisation. Machine and deep learning can improve retrospective and prospective justification auditing for better implementation of imaging referral guidelines.

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Impact and effect of imaging referral guidelines on patients and radiology services: a systematic review

Tay,

Foley,

Killeen

et al. 2024

Eur Radiol

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Objectives The objective of this systematic review was to offer a comprehensive overview and explore the associated outcomes from imaging referral guidelines on various key stakeholders, such as patients and radiologists. Materials and methods An electronic database search was conducted in Medline, Embase and Web of Science to retrieve citations published between 2013 and 2023. The search was constructed using medical subject headings and keywords. Only full-text articles and reviews written in English were included. The quality of the included papers was assessed using the mixed methods appraisal tool. A narrative synthesis was undertaken for the selected articles. Results The search yielded 4384 records. Following the abstract, full-text screening, and removal of duplication, 31 studies of varying levels of quality were included in the final analysis. Imaging referral guidelines from the American College of Radiology were most commonly used. Clinical decision support systems were the most evaluated mode of intervention, either integrated or standalone. Interventions showed reduced patient radiation doses and waiting times for imaging. There was a general reduction in radiology workload and utilisation of diagnostic imaging. Low-value imaging utilisation decreased with an increase in the appropriateness of imaging referrals and ratings and cost savings. Clinical effectiveness was maintained during the intervention period without notable adverse consequences. Conclusion Using evidence-based imaging referral guidelines improves the quality of healthcare and outcomes while reducing healthcare costs. Imaging referral guidelines are one essential component of improving the value of radiology in the healthcare system. Clinical relevance statement There is a need for broader dissemination of imaging referral guidelines to healthcare providers globally in tandem with the harmonisation of the application of these guidelines to improve the overall value of radiology within the healthcare system. Key Points The application of imaging referral guidelines has an impact and effect on patients, radiologists, and health policymakers. The adoption of imaging referral guidelines in clinical practice can impact healthcare costs and improve healthcare quality and outcomes. Implementing imaging referral guidelines contributes to the attainment of value-based radiology.

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Measuring appropriateness of diagnostic imaging: a scoping review

Cited by 5 publications

References 108 publications

Low-dose exposure to malathion and radiation results in the dysregulation of multiple neuronal processes, inducing neurotoxicity and neurodegeneration in mouse

Low-dose exposure to malathion and radiation results in the dysregulation of multiple neuronal processes, inducing neurotoxicity and neurodegeneration in mouse

Machine learning and deep learning for classifying the justification of brain CT referrals

Impact and effect of imaging referral guidelines on patients and radiology services: a systematic review

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