Pulmonary contusion: automated deep learning-based quantitative visualization

Sarkar, Nathan; Zhang, Lei; Campbell, Peter; Liang, Yuanyuan; Li, Guang; Khedr, Milad; Khetan, U.; Dreizin, David

doi:10.1007/s10140-023-02149-2

Cited by 8 publications

(3 citation statements)

References 48 publications

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“…Commercial CT-based tools are available for triage or detection and grading of intracranial hemorrhage and spine fractures. Proof-of-concept tools in earlier stages of the R&D pipeline include those for detection of active hemorrhage and aortic injury; detection and grading of pelvic fractures; detection, quanti cation, and grading of solid organ lacerations; as well as detection and quanti cation of traumatic extraperitoneal pelvic hematoma, hemoperitoneum, hemothorax, and pulmonary contusion [22,[42][43][44][45][46][47][48][49][50][51][52][53][54][55][56].…”

Section: Discussionmentioning

confidence: 99%

Does Acuity and Severity of Injury Affect Trauma Whole-Body CT Report Turnaround Time? A Large-scale Study

Sarkar,

Khedr,

Dreizin

2023

Preprint

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Trauma is a leading cause of death in the United States. Whole-Body CT is routinely used to screen for major injuries and hemorrhage and to plan interventions. Report Turn-Around-Time (RTAT) plays a role in the flow of patient management and is an objective marker of efficiency that could be improved upon with development and implementation of AI CAD tools for trauma patients. Currently, the degree to which this metric is affected by injury acuity and severity is poorly documented. The study included 11,251 patients who underwent admission trauma whole-body CT at two trauma centers within a single hospital system; a level I trauma center (n=9043) and a regional urban level II trauma center (n=2208). Patients were selected between July 2016 and September 2022. Clinical data and RTAT were extracted from the trauma registry and medical records. Overall median RTAT was 24 minutes (IQR: 4-48). RTAT was significantly longer in those who had hemodynamic shock (37 minutes vs 22 minutes, p<0.0001), polytrauma (ISS≥16) (34 minutes vs 21 minutes, p<0.0001), or massive transfusion (47.5 minutes vs 24 minutes, p<0.0001); those who expired (41 minutes vs 23 minutes,p<0.0001); the older cohort (age>55) (28 minutes vs 22 minutes, p<0.0001), and those with penetrating vs blunt injuries (27 minutes vs 23 minutes, p=0.001). Overall, these findings indicate that increased traumatic injury severity is associated with non-negligible increases in whole-body CT RTATs. The results provide strong justification for computer-aided detection/diagnosis (CAD) research and development to reduce cognitive load and augment diagnostic efficiency in severely injured patients who stand to benefit most from such tools.

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Section: Discussionmentioning

confidence: 99%

Does Acuity and Severity of Injury Affect Trauma Whole-Body CT Report Turnaround Time? A Large-scale Study

Sarkar,

Khedr,

Dreizin

2023

Preprint

Self Cite

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“…The model consisted of a fully automated ML and radiomics-based approach that showed a higher accuracy compared to an established score (ISS and abbreviated injury score of the thorax) to identify ARDS in trauma patients [18]. In this line, a rapid automated lung CT volumetry assessment of pulmonary contusions in trauma patients showed a good accuracy in assessing the risk of ARDS, length of intensive care stay, and time on mechanical ventilation [19].…”

Section: Prediction Of Ardsmentioning

confidence: 99%

Lung Imaging and Artificial Intelligence in ARDS

Chiumello,

Coppola,

Catozzi

et al. 2024

JCM

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Artificial intelligence (AI) can make intelligent decisions in a manner akin to that of the human mind. AI has the potential to improve clinical workflow, diagnosis, and prognosis, especially in radiology. Acute respiratory distress syndrome (ARDS) is a very diverse illness that is characterized by interstitial opacities, mostly in the dependent areas, decreased lung aeration with alveolar collapse, and inflammatory lung edema resulting in elevated lung weight. As a result, lung imaging is a crucial tool for evaluating the mechanical and morphological traits of ARDS patients. Compared to traditional chest radiography, sensitivity and specificity of lung computed tomography (CT) and ultrasound are higher. The state of the art in the application of AI is summarized in this narrative review which focuses on CT and ultrasound techniques in patients with ARDS. A total of eighteen items were retrieved. The primary goals of using AI for lung imaging were to evaluate the risk of developing ARDS, the measurement of alveolar recruitment, potential alternative diagnoses, and outcome. While the physician must still be present to guarantee a high standard of examination, AI could help the clinical team provide the best care possible.

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“…The model consisted in a fully automated ML and radiomics based approach, that had a higher accuracy compared to an established score (ISS and abbreviated injury score of the thorax) to identify ARDS in trauma patients [8]. In this line, a rapid automated chest CT volumetry assessment of pulmonary contusion in trauma patients showed a good accuracy to assess the risk of ARDS, length of intensive care and time on mechanical ventilation [10].…”

Section: Prediction Of Ardsmentioning

confidence: 99%

Lung Imaging and Artificial Intellingence in ARDS

Chiumello,

Coppola,

Catozzi

et al. 2023

Preprint

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The artificial intelligence (AI) is a machine or computing platform that is capable of making intelligent decision similarly to the human mind. The AI could improve diagnosis, treatment prognosis, and clinical workflow, particularly in the field of radiology. Concerning acute respiratory distress syndrome (ARDS) is a rather heterogeneous syndrome characterized by an inflammatory lung edema leading to an increased lung weight, decreased lung aeration with the presence of alveolar collapse and interstitial opacities mainly in the dependent area. Thus, lung imaging is an essential tool to assess not only the morphology but also the mechanical characteristics of ARDS patients. Chest computed tomography (CT) and ultrasound have a higher sensitivity and specificity than conventional chest radiography. This narrative review summarizes the state of art of AI in the field of lung imaging, focusing on CT and ultrasound technique in ARDS patients. A total number of 18 articles were retrieved. The application of AI in lung imaging was mainly devoted to assess the prediction of ARDS, the quantification of alveolar recruitment, the possible alternative diagnosis and the outcome. Although the presence of a physician is still essential to ensure a high quality of examinations the AI could assist the clinical team to provide the best possible care.

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Pulmonary contusion: automated deep learning-based quantitative visualization

Cited by 8 publications

References 48 publications

Does Acuity and Severity of Injury Affect Trauma Whole-Body CT Report Turnaround Time? A Large-scale Study

Does Acuity and Severity of Injury Affect Trauma Whole-Body CT Report Turnaround Time? A Large-scale Study

Lung Imaging and Artificial Intelligence in ARDS

Lung Imaging and Artificial Intellingence in ARDS

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