Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study

Maghami, Masoud; Sattari, Shahab Aldin; Tahmasbi, Marziyeh; Panahi, Pegah; Mozafari, Javad; Shirbandi, Kiarash

doi:10.1186/s12938-023-01172-1

Cited by 5 publications

(2 citation statements)

References 68 publications

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“…ML-aided tasks have already been incorporated into clinical practice, especially in imaging interpretation [ 32 , 33 , 34 ]. Although they are still imperfect and require a skilled supervisor, they are considered acceptable when rapid image feedback is needed and local expertise is lacking [ 10 ].…”

Section: Artificial Intelligence and Subfieldsmentioning

confidence: 99%

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Di Sarno,

Caroselli,

Tonin

et al. 2024

Biomedicines

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The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.

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Section: Artificial Intelligence and Subfieldsmentioning

confidence: 99%

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Di Sarno,

Caroselli,

Tonin

et al. 2024

Biomedicines

View full text Add to dashboard Cite

show abstract

“…ML-aided tasks have already been incorporated into clinical practice, especially in imaging interpretation [29][30][31]. Although they are still imperfect and require a skilled supervisor, they are considered acceptable when rapid image feedback is needed and local expertise is lacking [9].…”

mentioning

confidence: 99%

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Di Sarno,

Caroselli,

Tonin

et al. 2024

Preprint

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The dawn of Artificial Intelligence (AI) in healthcare stands as a milestone in medical innovation. A plethora of different medical fields are heavily involved and pediatric emergency medicine is no exception. These new tools do not merely provide more advanced and efficient systems for patients’ diagnosis, management and treatment. They rather concern a strict shift from traditional methods based upon broad categories towards a more personalized healthcare. AI offers many promises in pediatric emergency medicine with a wide range of applications involving clinical decision making, patients’ flows management and prioritization. Main barriers to a widespread diffusion involve technological challenges but also ethical issues and the paucity of extensive datasets in pediatric contexts. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies, pointing out the actual applications and challenges until future feasible research perspectives.

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Accuracy and time efficiency of a novel deep learning algorithm for Intracranial Hemorrhage detection in CT Scans

D’Angelo,

Bucolo,

Kamareddine

et al. 2024

Radiol med

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Purpose To evaluate a deep learning-based pipeline using a Dense-UNet architecture for the assessment of acute intracranial hemorrhage (ICH) on non-contrast computed tomography (NCCT) head scans after traumatic brain injury (TBI). Materials and methods This retrospective study was conducted using a prototype algorithm that evaluated 502 NCCT head scans with ICH in context of TBI. Four board-certified radiologists evaluated in consensus the CT scans to establish the standard of reference for hemorrhage presence and type of ICH. Consequently, all CT scans were independently analyzed by the algorithm and a board-certified radiologist to assess the presence and type of ICH. Additionally, the time to diagnosis was measured for both methods. Results A total of 405/502 patients presented ICH classified in the following types: intraparenchymal (n = 172); intraventricular (n = 26); subarachnoid (n = 163); subdural (n = 178); and epidural (n = 15). The algorithm showed high diagnostic accuracy (91.24%) for the assessment of ICH with a sensitivity of 90.37% and specificity of 94.85%. To distinguish the different ICH types, the algorithm had a sensitivity of 93.47% and a specificity of 99.79%, with an accuracy of 98.54%. To detect midline shift, the algorithm had a sensitivity of 100%. In terms of processing time, the algorithm was significantly faster compared to the radiologist’s time to first diagnosis (15.37 ± 1.85 vs 277 ± 14 s, p < 0.001). Conclusion A novel deep learning algorithm can provide high diagnostic accuracy for the identification and classification of ICH from unenhanced CT scans, combined with short processing times. This has the potential to assist and improve radiologists’ ICH assessment in NCCT scans, especially in emergency scenarios, when time efficiency is needed.

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Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study

Cited by 5 publications

References 68 publications

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Accuracy and time efficiency of a novel deep learning algorithm for Intracranial Hemorrhage detection in CT Scans

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