Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma.
Maria Chiara BrunesePasquale AvellaMicaela CappuccioSalvatore SpieziaGiulia PacellaPaolo BiancoSara GrecoLuigi RicciardelliNicola Maria LucarelliCorrado CaiazzoGianfranco VallonePublished in: Journal of personalized medicine (2024)
Background : Acute liver injury occurs most frequently due to trauma, but it can also occur because of sepsis or drug-induced injury. This review aims to analyze artificial intelligence (AI)'s ability to detect and quantify liver injured areas in adults and pediatric patients. Methods : A literature analysis was performed on the PubMed Dataset. We selected original articles published from 2018 to 2023 and cohorts with ≥10 adults or pediatric patients. Results : Six studies counting 564 patients were collected, including 170 (30%) children and 394 adults. Four (66%) articles reported AI application after liver trauma, one (17%) after sepsis, and one (17%) due to chemotherapy. In five (83%) studies, Computed Tomography was performed, while in one (17%), FAST-UltraSound was performed. The studies reported a high diagnostic performance; in particular, three studies reported a specificity rate > 80%. Conclusions : Radiomics models seem reliable and applicable to clinical practice in patients affected by acute liver injury. Further studies are required to achieve larger validation cohorts.
Keyphrases
- drug induced
- liver injury
- artificial intelligence
- end stage renal disease
- computed tomography
- case control
- newly diagnosed
- ejection fraction
- chronic kidney disease
- clinical practice
- magnetic resonance imaging
- liver failure
- machine learning
- deep learning
- big data
- prognostic factors
- randomized controlled trial
- acute kidney injury
- young adults
- squamous cell carcinoma
- patient reported outcomes
- respiratory failure
- contrast enhanced
- pet ct
- ultrasound guided
- radiation therapy
- acute respiratory distress syndrome
- patient reported
- aortic dissection