Emergency and Trauma Imaging

Patlas, Michael N.; Katz, Douglas S.; Odedra, Devang

doi:10.1007/978-3-030-44092-3_1-2

Cited by 2 publications

(2 citation statements)

References 47 publications

(47 reference statements)

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“…The development of local guidelines on imaging protocol to be used in abdominal trauma patients, creation of centres of reference, improved workforce education and training, quality management, upscaling, and financing are other suggested solutions. [ 5 11 12 ]…”

Section: Discussionmentioning

confidence: 99%

Abdominal trauma imaging in a Nigerian tertiary hospital—Our experience with 87 adult patients

Adenuga¹,

Adeyeye

2023

J West Afr Coll Surg

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Introduction: Abdominal trauma is a major cause of morbidity and mortality in low- and medium-income countries (LMICs). Abdominal trauma imaging is important in determining the location and severity of organ injury, the need for surgery, and the identification of complications. The choice of imaging in abdominal trauma in LMICs is influenced by peculiar problems, which include the availability of imaging modality, expertise, and cost. There are few reports on options of trauma imaging in LMICs, and this study aimed to identify and characterise the type of imaging done for patients presenting with abdominal trauma at the University of Ilorin Teaching Hospital. Materials and Methods: This was a retrospective observational study of patients with abdominal trauma who presented at the University of Ilorin Teaching Hospital from 2013 to 2019. Records were identified, and data were extracted and analysed. Results: A total of 87 patients were included in the study. There were 73 males and 14 females. The abdominal ultrasound was the commonest modality done in 36 (41%) patients, whereas abdominal computed tomography was done in five (6%) patients. Eleven patients (13%) had no imaging done, and 10 of these patients proceeded to having surgery. In patients with intraoperative finding of perforated viscus, the sensitivity of radiography was 85% and specificity was 100%, whereas that of the ultrasound was 86.7% and 50% for sensitivity and specificity, respectively. The ultrasound scan was the commonest imaging done for patients who presented with features of haemorrhage ( P = 0.04), odds ratio (OR) = 1.29 (95% confidence interval [CI] = 1.08–1.6), and patients with severe injury, P = 0.03, OR = 2.07 (95% CI = 1.06–4.06). Gender ( P = 0.64), shock at presentation ( P = 0.19), and mechanism of injury ( P = 0.11) did not influence the choice of imaging. Conclusions: Abdominal trauma imaging in this setting was majorly via ultrasound and abdominal radiographs. Factors suggested to influence the pattern of abdominal trauma imaging in LMICs include the availability of specific imaging modality, cost, lack of standardisation and definite abdominal trauma protocols.

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Section: Discussionmentioning

confidence: 99%

Abdominal trauma imaging in a Nigerian tertiary hospital—Our experience with 87 adult patients

Adenuga¹,

Adeyeye

2023

J West Afr Coll Surg

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“…Despite the importance of this speciality and the volume of radiological tests that exist, the lack of radiologists able to interpret them (7)(8)(9)(10)(11), as well as the increase in errors in interpretation as a consequence of a heavy workload (12)(13)(14)(15) is becoming increasingly evident. Furthermore, radiological diagnosis is a component of clinical competencies in various specialities, including family and community medicine.…”

Section: Introductionmentioning

confidence: 99%

External Validation of an Artificial Intelligence Algorithm for the Analysis of Chest X-Rays in the Primary Care Setting

Catalina,

Vidal-Alaball,

Fuster-Casanovas

et al. 2023

Preprint

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Interpreting chest X-rays is a complex task, and artificial intelligence algorithms for this purpose are currently being developed. It is important to perform external validations of these algorithms in order to implement them. This study therefore aims to externally validate an AI algorithm's diagnoses in real clinical practice, comparing them to a radiologist’s diagnoses. The aim is also to identify diagnoses the algorithm may not have been trained for. A prospective observational study for the external validation of the AI algorithm in a region of Catalonia, comparing the AI algorithm’s diagnosis with that of the reference radiologist, considered the gold standard. The external validation was performed with a sample of 278 images and reports, 51.8% of which showed no radiological abnormalities according to the radiologist's report. Analysing the validity of the AI algorithm, the average accuracy was 0.95 (95% CI 0.92; 0.98), the sensitivity was 0.48 (95% CI 0.30; 0.66) and the specificity was 0.98 (95% CI 0.97; 0.99). The conditions where the algorithm was most sensitive were external, upper abdominal and cardiac and/or valvular implants. On the other hand, the conditions where the algorithm was less sensitive were in the mediastinum, vessels and bone. The AI algorithm has proven to be useful when identifying images with or without conditions. However, in order to be a valuable tool to help and support experts, it requires additional real-world training to enhance its diagnostic capabilities for some of the conditions analysed.

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Emergency and Trauma Imaging

Cited by 2 publications

References 47 publications

Abdominal trauma imaging in a Nigerian tertiary hospital—Our experience with 87 adult patients

Abdominal trauma imaging in a Nigerian tertiary hospital—Our experience with 87 adult patients

External Validation of an Artificial Intelligence Algorithm for the Analysis of Chest X-Rays in the Primary Care Setting

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