Leen S. Alhadlaq

Background/Objectives: Since thoracic endovascular aortic repair (TEVAR) and endovascular aneurysm repair (EVAR) are increasingly utilized, examining their outcomes and safety in real-world scenarios is crucial. This study investigated the management and outcomes of TEVAR and EVAR as alternatives to traditional open surgical repair for managing aortic pathologies. Methods: This was a retrospective cohort study. We analyzed the data from 59 consecutive patients who underwent TEVAR or EVAR between 2015 and 2022 at a single tertiary care center. The primary outcome was survival, and secondary outcomes were complications assessment, including re-intervention and occurrence of endoleaks. Results: TEVAR accounted for 47.5% of cases (n = 28), while EVAR comprised 52.5% (n = 31). Patients were mostly 61–70 years old (23.7%) and male (91.5%). Surgery indications differed, with aneurysmal repair being the prevalent indication for EVAR (90.3%, n = 28) and trauma being the main indication for TEVAR (67.9%, n = 19). Regarding the primary outcome, 11 patients (18.6%) died for various reasons; of those, 2 patients (3.4%) were determined to have died from vascular-related issues. Most patients (81.4%, n = 48) did not experience intraoperative complications. The most common intraoperative complications were endoleaks and access failure, each affecting 5.1% (n = 3) of patients. Re-intervention was necessary in 16.9% (n = 10) of cases, with endoleaks being the major indication (60%). Emergency intervention was more frequent in the TEVAR group (p = 0.013), resulting in significantly longer hospitalization (p = 0.012). Conclusions: Despite limitations, our analysis indicates a good safety profile with high success rates and a low incidence of adverse health outcomes and mortality in TEVAR/EVAR procedures. Nevertheless, the results emphasize the ongoing concern of endograft leaks, necessitating re-interventions.

Background: Artificial intelligence (AI) is widely used in various medical fields, including diagnostic radiology as a tool for greater efficiency, precision, and accuracy. The integration of AI as a radiological diagnostic tool has the potential to mitigate delays in diagnosis, which could, in turn, impact patients’ prognosis and treatment outcomes. The literature shows conflicting results regarding patients’ attitudes to AI as a diagnostic tool. To the best of our knowledge, no similar study has been conducted in Saudi Arabia. Objective: The objectives of this study are to examine patients’ attitudes toward the use of AI as a tool in diagnostic radiology at King Khalid University Hospital, Saudi Arabia. Additionally, we sought to explore potential associations between patients’ attitudes and various sociodemographic factors. Methods: This descriptive-analytical cross-sectional study was conducted in a tertiary care hospital. Data were collected from patients scheduled for radiological imaging through a validated self-administered questionnaire. The main outcome was to measure patients’ attitudes to the use of AI in radiology by calculating mean scores of 5 factors, distrust and accountability (factor 1), procedural knowledge (factor 2), personal interaction and communication (factor 3), efficiency (factor 4), and methods of providing information to patients (factor 5). Data were analyzed using the student t test, one-way analysis of variance followed by post hoc and multivariable analysis. Results: A total of 382 participants (n=273, 71.5% women and n=109, 28.5% men) completed the surveys and were included in the analysis. The mean age of the respondents was 39.51 (SD 13.26) years. Participants favored physicians over AI for procedural knowledge, personal interaction, and being informed. However, the participants demonstrated a neutral attitude for distrust and accountability and for efficiency. Marital status was found to be associated with distrust and accountability, procedural knowledge, and personal interaction. Associations were also found between self-reported health status and being informed and between the field of specialization and distrust and accountability. Conclusions: Patients were keen to understand the work of AI in radiology but favored personal interaction with a radiologist. Patients were impartial toward AI replacing radiologists and the efficiency of AI, which should be a consideration in future policy development and integration. Future research involving multicenter studies in different regions of Saudi Arabia is required.

BACKGROUND Artificial intelligence (AI) is widely used in various medical fields, including diagnostic radiology as a tool for greater efficiency, precision, and accuracy. The integration of AI as a radiological diagnostic tool has the potential to mitigate delays in diagnosis, which could, in turn, impact patients’ prognosis and treatment outcomes. The literature shows conflicting results regarding patients’ attitudes to AI as a diagnostic tool. To the best of our knowledge, no similar study has been conducted in Saudi Arabia. OBJECTIVE The objectives of this study are to examine patients’ attitudes toward the use of AI as a tool in diagnostic radiology at King Khalid University Hospital, Saudi Arabia. Additionally, we sought to explore potential associations between patients’ attitudes and various sociodemographic factors. METHODS This descriptive-analytical cross-sectional study was conducted in a tertiary care hospital. Data were collected from patients scheduled for radiological imaging through a validated self-administered questionnaire. The main outcome was to measure patients’ attitudes to the use of AI in radiology by calculating mean scores of 5 factors, distrust and accountability (factor 1), procedural knowledge (factor 2), personal interaction and communication (factor 3), efficiency (factor 4), and methods of providing information to patients (factor 5). Data were analyzed using the student t test, one-way analysis of variance followed by post hoc and multivariable analysis. RESULTS A total of 382 participants (n=273, 71.5% women and n=109, 28.5% men) completed the surveys and were included in the analysis. The mean age of the respondents was 39.51 (SD 13.26) years. Participants favored physicians over AI for procedural knowledge, personal interaction, and being informed. However, the participants demonstrated a neutral attitude for distrust and accountability and for efficiency. Marital status was found to be associated with distrust and accountability, procedural knowledge, and personal interaction. Associations were also found between self-reported health status and being informed and between the field of specialization and distrust and accountability. CONCLUSIONS Patients were keen to understand the work of AI in radiology but favored personal interaction with a radiologist. Patients were impartial toward AI replacing radiologists and the efficiency of AI, which should be a consideration in future policy development and integration. Future research involving multicenter studies in different regions of Saudi Arabia is required.