Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT

This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly (p < 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group (p < 0.05), similar in obese but significantly different in non-obese patients (p < 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions (p < 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r > 0.84; ICC > 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists' efficiency in clinical practice.

Feasibility of Coronary CT Angiography-derived Left Ventricular Long-Axis Shortening as an Early Marker of Ventricular Dysfunction in Transcatheter Aortic Valve Replacement

Purpose

To evaluate the value of using left ventricular (LV) long-axis shortening (LAS) derived from coronary CT angiography (CCTA) to predict mortality in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR).

Materials and Methods

Patients with severe AS who underwent CCTA for preprocedural TAVR planning between September 2014 and December 2019 were included in this retrospective study. CCTA covered the whole cardiac cycle in 10% increments. Image series reconstructed at end systole and end diastole were used to measure LV-LAS. All-cause mortality within 24 months of follow-up after TAVR was recorded. Cox regression analysis was performed, and hazard ratios (HRs) are presented with 95% CIs. The C index was used to evaluate model performance, and the likelihood ratio χ² test was performed to compare nested models.

Results

The study included 175 patients (median age, 79 years [IQR, 73-85 years]; 92 men). The mortality rate was 22% (38 of 175). When adjusting for predictive clinical confounders, it was found that LV-LAS could be used independently to predict mortality (adjusted HR, 2.83 [95% CI: 1.13, 7.07]; P = .03). In another model using the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM), LV-LAS remained significant (adjusted HR, 3.38 [95 CI: 1.48, 7.72]; P = .004), and its use improved the predictive value of the STS-PROM, increasing the STS-PROM C index from 0.64 to 0.71 (χ² = 29.9 vs 19.7, P = .001). In a subanalysis of patients with a normal LV ejection fraction (LVEF), the significance of LV-LAS persisted (adjusted HR, 3.98 [95 CI: 1.56, 10.17]; P = .004).

Conclusion

LV-LAS can be used independently to predict mortality in patients undergoing TAVR, including those with a normal LVEF.Keywords: CT Angiography, Transcatheter Aortic Valve Implantation/Replacement (TAVI/TAVR), Cardiac, Outcomes Analysis, Cardiomyopathies, Left Ventricle, Aortic Valve Supplemental material is available for this article. © RSNA, 2022See also the commentary by Everett and Leipsic in this issue.

Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease: A Noninferiority Study

OBJECTIVES

The aim of the study is to investigate the performance of artificial intelligence (AI) convolutional neural networks (CNN) in detecting lung nodules on chest computed tomography of patients with complex lung disease, and demonstrate its noninferiority when compared against an experienced radiologist through clinically relevant assessments.

METHODS

A CNN prototype was used to retrospectively evaluate 103 complex lung disease cases and 40 control cases without reported nodules. Computed tomography scans were blindly evaluated by an expert thoracic radiologist; a month after initial analyses, 20 positive cases were re-evaluated with the assistance of AI. For clinically relevant applications: (1) AI was asked to classify each patient into nodules present or absent and (2) AI results were compared against standard radiology reports. Standard statistics were performed to determine detection performance.

RESULTS

AI was, on average, 27 seconds faster than the expert and detected 8.4% of nodules that would have been missed. AI had a sensitivity of 67.7%, similar to an accuracy reported for experienced radiologists. AI correctly classified each patient (nodules present/absent) with a sensitivity of 96.1%. When matched against radiology reports, AI performed with a sensitivity of 89.4%. Control group assessment demonstrated an overall specificity of 82.5%. When aided by AI, the expert decreased the average assessment time per case from 2:44 minutes to 35.7 seconds, while reporting an overall increase in confidence.

CONCLUSION

In a group of patients with complex lung disease, the sensitivity of AI is similar to an experienced radiologist and the tool helps detect previously missed nodules. AI also helps experts analyze for lung nodules faster and more confidently, a feature that is beneficial to patients and favorable to hospitals due to increased patient load and need for shorter turnaround times.

Performance of an Artificial Intelligence-Based Platform Against Clinical Radiology Reports for the Evaluation of Noncontrast Chest CT

RATIONALE AND OBJECTIVES

Research on implementation of artificial intelligence (AI) in radiology workflows and its impact on reports remains scarce. In this study, we aim to assess if an AI platform would perform better than clinical radiology reports in evaluating noncontrast chest computed tomography (CT) scans.

MATERIALS AND METHODS

Consecutive patients who had undergone noncontrast chest CT were retrospectively identified. The radiology reports were reviewed in a binary fashion for reporting of pulmonary lesions, pulmonary emphysema, aortic dilatation, coronary artery calcifications (CAC), and vertebral compression fractures (VCF). CT scans were then processed using an AI platform. The reports' findings and the AI results were subsequently compared to a consensus read by two board-certificated radiologists as reference.

RESULTS

A total of 100 patients (mean age: 64.2 ± 14.8 years; 57% males) were included in this study. Aortic segmentation and calcium quantification failed to be processed by AI in 2 and 3 cases, respectively. AI showed superior diagnostic performance in identifying aortic dilatation (AI: sensitivity: 96.3%, specificity: 81.4%, AUC: 0.89) vs (Reports: sensitivity: 25.9%, specificity: 100%, AUC: 0.63), p <0.001; and CAC (AI: sensitivity: 89.8%, specificity: 100, AUC: 0.95) vs (Reports: sensitivity: 75.4%, specificity: 94.9%, AUC: 0.85), p = 0.005. Reports had better performance than AI in identifying pulmonary lesions (Reports: sensitivity: 97.6%, specificity: 100%, AUC: 0.99) vs (AI: sensitivity: 92.8%, specificity: 82.4%, AUC: 0.88), p = 0.024; and VCF (Reports: sensitivity:100%, specificity: 100%, AUC: 1.0) vs (AI: sensitivity: 100%, specificity: 63.7%, AUC: 0.82), p <0.001. A comparable diagnostic performance was noted in identifying pulmonary emphysema on AI (sensitivity: 80.6%, specificity: 66.7%. AUC: 0.74) and reports (sensitivity: 74.2%, specificity: 97.1%, AUC: 0.86), p = 0.064.

CONCLUSION

Our results demonstrate that incorporating AI support platforms into radiology workflows can provide significant added value to clinical radiology reporting.

Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes

BACKGROUND

Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine NCCT and evaluate prediction of cardiovascular outcomes.

METHODS

We retrospectively evaluated 273 patients (median age 69 years, 55.5% male) who underwent LDCT for lung cancer screening. LA volumes were quantified by three expert cardiothoracic radiologists and a prototype AI algorithm. LA volumes were then indexed to the body surface area (BSA). Expert and AI LA volume index (LAVi) were compared and used to predict cardiovascular outcomes within five years. Logistic regression with appropriate univariate statistics were used for modelling outcomes.

RESULTS

There was excellent correlation between AI and expert results with an LAV intraclass correlation of 0.950 (0.936-0.960). Bland-Altman plot demonstrated the AI underestimated LAVi by a mean 5.86 ​mL/m². AI-LAVi was associated with new-onset atrial fibrillation (AUC 0.86; OR 1.12, 95% CI 1.08-1.18, p ​< ​0.001), HF hospitalization (AUC 0.90; OR 1.07, 95% CI 1.04-1.13, p ​< ​0.001), and MACCE (AUC 0.68; OR 1.04, 95% CI 1.01-1.07, p ​= ​0.01).

CONCLUSION

This novel deep learning algorithm for automated measurement of LA volume on lung cancer screening scans had excellent agreement with manual quantification. AI-LAVi is significantly associated with increased risk of new-onset atrial fibrillation, HF hospitalization, and major adverse cardiac and cerebrovascular events within 5 years.

Coronary CT Fractional Flow Reserve before Transcatheter Aortic Valve Replacement: Clinical Outcomes

Background The role of CT angiography-derived fractional flow reserve (CT-FFR) in pre-transcatheter aortic valve replacement (TAVR) assessment is uncertain. Purpose To evaluate the predictive value of on-site machine learning-based CT-FFR for adverse clinical outcomes in candidates for TAVR. Materials and Methods This observational retrospective study included patients with severe aortic stenosis referred to TAVR after coronary CT angiography (CCTA) between September 2014 and December 2019. Clinical end points comprised major adverse cardiac events (MACE) (nonfatal myocardial infarction, unstable angina, cardiac death, or heart failure admission) and all-cause mortality. CT-FFR was obtained semiautomatically using an on-site machine learning algorithm. The ability of CT-FFR (abnormal if ≤0.75) to predict outcomes and improve the predictive value of the current noninvasive work-up was assessed. Survival analysis was performed, and the C-index was used to assess the performance of each predictive model. To compare nested models, the likelihood ratio χ² test was performed. Results A total of 196 patients (mean age ± standard deviation, 75 years ± 11; 110 women [56%]) were included; the median time of follow-up was 18 months. MACE occurred in 16% (31 of 196 patients) and all-cause mortality in 19% (38 of 196 patients). Univariable analysis revealed CT-FFR was predictive of MACE (hazard ratio [HR], 4.1; 95% CI: 1.6, 10.8; P = .01) but not all-cause mortality (HR, 1.2; 95% CI: 0.6, 2.2; P = .63). CT-FFR was independently associated with MACE (HR, 4.0; 95% CI: 1.5, 10.5; P = .01) when adjusting for potential confounders. Adding CT-FFR as a predictor to models that include CCTA and clinical data improved their predictive value for MACE (P = .002) but not all-cause mortality (P = .67), and it showed good discriminative ability for MACE (C-index, 0.71). Conclusion CT angiography-derived fractional flow reserve was associated with major adverse cardiac events in candidates for transcatheter aortic valve replacement and improved the predictive value of coronary CT angiography assessment. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Choe in this issue.

Compressed sensing acceleration of cardiac cine imaging allows reliable and reproducible assessment of volumetric and functional parameters of the left and right atrium

Objectives

To compare volumetric and functional parameters of the atria derived from highly accelerated compressed sensing (CS)–based cine sequences in comparison to conventional (Conv) cine imaging.

Methods

CS and Conv cine sequences were acquired in 101 subjects (82 healthy volunteers (HV) and 19 patients with heart failure with reduced ejection fraction (HFrEF)) using a 3T MR scanner in this single-center study. Time-volume analysis of the left (LA) and right atria (RA) were performed in both sequences to evaluate atrial volumes and function (total, passive, and active emptying fraction). Inter-sequence and inter- and intra-reader agreement were analyzed using correlation, intraclass correlation (ICC), and Bland-Altman analysis.

Results

CS-based cine imaging led to a 69% reduction of acquisition time. There was significant difference in atrial parameters between CS and Conv cine, e.g., LA minimal volume (LAVmin) (Conv 24.0 ml (16.7–32.7), CS 25.7 ml (19.2–35.2), p < 0.0001) or passive emptying fraction (PEF) (Conv 53.9% (46.7–58.4), CS 49.0% (42.0–54.1), p < 0.0001). However, there was high correlation between the techniques, yielding good to excellent ICC (0.76–0.99) and small mean of differences in Bland-Altman analysis (e.g. LAVmin − 2.0 ml, PEF 3.3%). Measurements showed high inter- (ICC > 0.958) and intra-rater (ICC > 0.934) agreement for both techniques. CS-based parameters (PEF AUC = 0.965, LAVmin AUC = 0.864) showed equivalent diagnostic ability compared to Conv cine imaging (PEF AUC = 0.989, LAVmin AUC = 0.859) to differentiate between HV and HFrEF.

Conclusion

Atrial volumetric and functional evaluation using CS cine imaging is feasible with relevant reduction of acquisition time, therefore strengthening the role of CS in clinical CMR for atrial imaging.

Key Points

• Reliable assessment of atrial volumes and function based on compressed sensing cine imaging is feasible.

• Compressed sensing reduces scan time and has the potential to overcome obstacles of conventional cine imaging.

• No significant differences for subjective image quality, inter- and intra-rater agreement, and ability to differentiate healthy volunteers and heart failure patients were detected between conventional and compressed sensing cine imaging.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07830-z.

Radiomics: The Next Frontier of Cardiac Computed Tomography

Radiomics uses advanced image analysis to extract massive amounts of quantitative information from digital images, which is not otherwise distinguishable to the human eye. The mined data can be used to explore and establish new and undiscovered correlations between these imaging features and clinical end points. Cardiac computed tomography (CT) is a first-line imaging modality for evaluating coronary artery disease and has a primary role in the assessment of cardiac structures. Conventional interpretation of cardiac CT images relies mostly on subjective and qualitative analysis, as well as basic geometric quantification. To date, some proof-of-concept studies have demonstrated the feasibility and diagnostic performance of cardiac CT radiomics analysis. This review describes the current literature on radiomics in cardiac CT and discusses its advantages, challenges, and future directions. Although much evidences are needed in this field, cardiac CT radiomics has a lot to offer to patients and physicians with potential to define cardiac disease phenotypes on imaging with higher precision.

Prevalence of Abnormal Coronary Findings on Coronary Computed Tomography Angiography Among Young Adults Presenting With Chest Pain

PURPOSE

We evaluated the prevalence of coronary stenosis on coronary computed tomography angiography (CCTA) in patients aged 18 to 30 years, who presented to the emergency department with chest pain. We also examined the risk factors potentially associated with abnormal coronary findings on CCTA in this age group.

MATERIALS AND METHODS

A total of 884 patients were retrospectively evaluated. Indication for CCTA was guided by our hospital's chest pain protocol based on ACC/AHA guidelines. These were performed using the standard technique and interpreted based on CAD-RADS guidelines. Scans were identified as abnormal if atherosclerotic coronary artery disease (CAD), myocardial bridging (MB), or any anatomic coronary artery anomaly were present.

RESULTS

Twenty-two percent of patients had a coronary abnormality on CCTA. The most common abnormality was MB (17.3%), followed by CAD (4.4%) and coronary anomalies (1.5%). A small minority had stenosis (2.8%), most commonly caused by CAD. Most cases with stenosis were minimal to mild (72%) with 0.8% having coronary stenosis ≥50%. Age and male sex were risk factors for both coronary artery stenosis (odds ratio: 1.32 and 4.50, 95% confidence interval: 1.03-1.69, and 1.23-16.46, P=0.028 and 0.023, respectively) and CAD (odds ratio: 1.52 and 3.67, 95% confidence interval: 1.14-2.04, and 1.26-10.66, P=0.005 and 0.017, respectively).

CONCLUSIONS

Epicardial coronary stenosis is rarely the cause of chest pain among young adult patients presenting to the emergency department. Age and male sex were both risk factors for coronary artery stenosis/disease in this age group.

Predictive value of initial imaging and staging with long-term outcomes in young adults diagnosed with colorectal cancer

PURPOSE

To evaluate how initial abdominopelvic CT findings and staging correlate with outcomes in a cohort of patients aged 18-40 years.

METHODS

We evaluated all young adult patients at a single tertiary center diagnosed with histopathologically confirmed CRC who also had CT of the abdomen and pelvis at the time of initial diagnosis. Demographics, symptoms, CT findings, staging, treatments, and outcomes at 1 year and 5 years were recorded.

RESULTS

Of 91 patients who met initial inclusion criteria, 81.8% had a mass present on CT, with an average size of 4.8 cm ± 2.9. A majority of patients were surgical stage III or IV (64.3%). Advanced AJCC stage was more common with rectal tumors and metastatic disease on initial CT (p < 0.0001). In a subgroup analysis, almost all patients initially staged 4A or higher had progression of disease. At the final follow-up visit, by RECIST 1.1 criteria, 58.8% had progressive disease, 35.3% complete response, and 3.9% stable disease. The overall 5-year survival rate in this subgroup was 40% with lower survival probability with increasing stage (p = 0.0001).

CONCLUSION

Most young adult patients presented with large tumors on imaging, increasing the likelihood of identification on CT. Tumors initially presenting in the rectum with enlarged lymph nodes and/or with distant metastases on CT were more often associated with advanced surgical stage and poorer prognosis. A majority of patients presented at an advanced stage, most commonly stage 4A, and had progression of disease at follow-up.

Measurement accuracy of prototype non-contrast, compressed sensing-based, respiratory motion-resolved whole heart cardiovascular magnetic resonance angiography for the assessment of thoracic aortic dilatation: comparison with computed tomography angiography

BACKGROUND

Patients with thoracic aortic dilatation who undergo annual computed tomography angiography (CTA) are subject to repeated radiation and contrast exposure. The purpose of this study was to evaluate the feasibility of a non-contrast, respiratory motion-resolved whole-heart cardiovascular magnetic resonance angiography (CMRA) technique against reference standard CTA, for the quantitative assessment of cardiovascular anatomy and monitoring of disease progression in patients with thoracic aortic dilatation.  METHODS: Twenty-four patients (68.6 ± 9.8 years) with thoracic aortic dilatation prospectively underwent clinical CTA and research 1.5T CMRA between July 2017 and November 2018. Scans were repeated in 15 patients 1 year later. A prototype free-breathing 3D radial balanced steady-state free-precession whole-heart CMRA sequence was used in combination with compressed sensing-based reconstruction. Area, circumference, and diameter measurements were obtained at seven aortic levels by two experienced and two inexperienced readers. In addition, area and diameter measurements of the cardiac chambers, pulmonary arteries and pulmonary veins were also obtained. Agreement between the two modalities was assessed with intraclass correlation coefficient (ICC) analysis, Bland-Altman plots and scatter plots.

RESULTS

Area, circumference and diameter measurements on a per-level analysis showed good or excellent agreement between CTA and CMRA (ICCs > 0.84). Means of differences on Bland-Altman plots were: area 0.0 cm2 [- 1.7; 1.6]; circumference 1.0 mm [- 10.0; 12.0], and diameter 0.6 mm [- 2.6; 3.6]. Area and diameter measurements of the left cardiac chambers showed good agreement (ICCs > 0.80), while moderate to good agreement was observed for the right chambers (all ICCs > 0.56). Similar good to excellent inter-modality agreement was shown for the pulmonary arteries and veins (ICC range 0.79-0.93), with the exception of the left lower pulmonary vein (ICC < 0.51). Inter-reader assessment demonstrated mostly good or excellent agreement for both CTA and CMRA measurements on a per-level analysis (ICCs > 0.64). Difference in maximum aortic diameter measurements at baseline vs follow up showed excellent agreement between CMRA and CTA (ICC = 0.91).

CONCLUSIONS

The radial whole-heart CMRA technique combined with respiratory motion-resolved reconstruction provides comparable anatomical measurements of the thoracic aorta and cardiac structures as the reference standard CTA. It could potentially be used to diagnose and monitor patients with thoracic aortic dilatation without exposing them to radiation or contrast media.

Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations

The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional benefits and potential of an artificial intelligence (AI) analysis platform for chest CT examinations in routine clinical practice will be examined. Specific application examples include AI-based, fully automatic lung segmentation with emphysema quantification, aortic measurements, detection of pulmonary nodules, and bone mineral density measurement. This contribution aims to appraise this AI-based application for value-added diagnosis during routine chest CT examinations and explore future development perspectives.

Marijuana use and coronary artery disease in young adults

Background

Marijuana is the most popular drug of abuse in the United States. The association between its use and coronary artery disease has not yet been fully elucidated. This study aims to determine the frequency of coronary artery disease among young to middle aged adults presenting with chest pain who currently use marijuana as compared to nonusers.

Methods

In this retrospective study, 1,420 patients with chest pain or angina equivalent were studied. Only men between 18 and 40 years and women between 18 and 50 years of age without history of cardiac disease were included. All patients were queried about current or prior cannabis use and underwent coronary CT angiography. Each coronary artery on coronary CT angiography was assessed based on the CAD-RADS reporting system.

Results

A total of 146 (10.3%) out of 1,420 patients with chest pain were identified as marijuana users. Only 6.8% of the 146 marijuana users had evidence of coronary artery disease on coronary CT angiography. In comparison, the rate was 15.0% among the 1,274 marijuana nonusers (p = 0.008). After accounting for other cardiac risk factors in a multivariate analysis, the negative association between marijuana use and coronary artery disease on coronary CT angiography diminished (p = 0.12, 95% CI 0.299–1.15). A majority of marijuana users were younger than nonusers and had a lower frequency of hypertension and diabetes than nonusers. There was no statistical difference in lipid panel values between the two groups. Only 2 out of 10 marijuana users with coronary artery disease on coronary CT angiography had hemodynamically significant stenosis.

Conclusion

Among younger patients being evaluated for chest pain, self-reported cannabis use conferred no additional risk of coronary artery disease as detected on coronary CT angiography.

Diagnostic performance of Gallium-68 prostate-specific membrane antigen positron emission tomography-computed tomography in intermediate and high risk prostate cancer

Gallium-68 prostate-specific membrane antigen positron emission tomography-computed tomography (Ga-68 PSMA PET/CT) is an imaging modality that promises improved sensitivity and specificity of detection of prostate cancer lesions based on their increased uptake of PSMA-based radiotracers. It remains an emerging modality that has not yet been endorsed in the guidelines for the management of prostate cancer pending more established evidence to prove its efficacy. The objective of the study is to assess the value of Ga-68 PSMA PET/CT in the detection and localization of patients diagnosed with intermediate or high risk prostate cancer.Twenty three patients with intermediate or high risk prostate cancer had undergone Ga-68 PSMA PET/CT imaging prior to robotic assisted radical prostatectomy. Surgical specimens were then submitted for histological examinations. Lesions visualized on PET/CT and histology were independently mapped unto a 36-segment (Prostate Imaging Reporting and Data System version 2 [PI-RADS v.2]) map of the prostate. Concordance of visualization on PET/CT as compared to the histology as gold standard reference was then assessed. Lesions visualized on PET/CT and histology were independently mapped unto a 36-segment (PI-RADS v.2) map of the prostate. Concordance of visualization on PET/CT as compared to the histology as gold standard reference was then assessed.Sensitivity for all lesions identified on Ga-68 PSMA PET/CT was 42.37%; specificity was 88.61%. Both parameters were higher when considering only index lesions for which sensitivity was 68.42% and specificity was 98.23%. Sensitivity for the index lesions in intermediate risk group was 53.2% and was higher in the high risk group reaching 83.33%.Ga-68 PSMA PET/CT provides accurate localization of tumor lesions in patients with intermediate and high risk prostate cancer.

Epidemiology and clinical characteristics of traumatic brain injury in Lebanon: A systematic review

BACKGROUND

Traumatic brain injury (TBI) is a debilitating medical and emerging public health problem that is affecting people worldwide due to a multitude of factors including both domestic and war-related acts. The objective of this paper is to systematically review the status of TBI in Lebanon - a Middle Eastern country with a weak health system that was chartered by several wars and intermittent outbursts of violence - in order to identify the present gaps in knowledge, direct future research initiatives and to assist policy makers in planning progressive and rehabilitative policies.

METHODS

OVID/Medline, PubMed, Scopus databases and Google Scholar were lastly searched on April 15, 2016 to identify all published research studies on TBI in Lebanon. Studies published in English, Arabic or French that assessed Lebanese patients afflicted by TBI in Lebanon were warranting inclusion in this review. Case reports, reviews, biographies and abstracts were excluded. Throughout the whole review process, reviewers worked independently and in duplicate during study selection, data abstraction and methodological assessment using the Downs and Black Checklist.

RESULTS

In total, 11 studies were recognized eligible as they assessed Lebanese patients afflicted by TBI on Lebanese soils. Considerable methodological variation was found among the identified studies. All studies, except for two that evaluated domestic causes such as falls, reported TBI due to war-related injuries. Age distribution of TBI victims revealed two peaks, young adults between 18 and 40 years, and older adults aged 60 years and above, where males constituted the majority. Only three studies reported rates of mild TBI. Mortality, rehabilitation and systemic injury rates were rarely reported and so were the complications involved; infections were an exception.

CONCLUSION

Apparently, status of TBI in Lebanon suffers from several gaps which need to be bridged through implementing more basic, epidemiological, clinical and translational research in this field in the future.