Purpose: It is well known that inguinal hernia repair in geriatric patients carries a higher risk of postoperative complications compared to younger patients. One of the key factors influencing these complications is the surgical technique employed. However, there is limited knowledge regarding the impact of laparoscopy on elderly patients. This prospective randomized study aims to compare the outcomes of laparoscopic and open hernia repair techniques in this patient population. Methods: Between April 2023 and April 2024, 120 consecutive patients aged 65 years and older with inguinal hernia were randomly assigned to one of two groups: the laparoscopic TEP group ( n = 60) and the open (Lichtenstein) procedure group ( n = 60). The study was registered at ClinicalTrials.gov (NCT06417346). The primary outcome of this study was the comparison of postoperative complication rates. Secondary outcomes included comparisons of hernia types, operative times, postoperative pain levels, and recurrence rates. Results: A total of 120 patients were followed up for 12 months. The mean age was 71.7 ± 6.5 years in the open group and 69.6 ± 3.9 years in the TEP group ( p = 0.18). The mean operative time was 35.8 ± 7.8 min in the open group and 36.3 ± 8.7 min in the TEP group ( p = 0.92). The mean time to return to normal daily activities was 10.6 ± 4.3 days in the open group and 7.5 ± 2.4 days in the TEP group ( p < 0.001). On postoperative day 1, the VAS score was 3.7 ± 1.3 in the open group and 2.9 ± 1.1 in the TEP group ( p < 0.001). At the end of the first month, the VAS score was 2.6 ± 1.0 in the open group and 1.7 ± 0.9 in the TEP group ( p < 0.001). Chronic pain was observed in 6 patients (10%) in the open group and 1 patient (1.7%) in the TEP group ( p = 0.05). No complications occurred in 51 patients (85%) in the open group and 52 patients (86.7%) in the TEP group ( p = 0.84). Recurrent inguinal hernia was detected in 4 patients (6.7%) in the open group and 1 patient (1.7%) in the TEP group ( p = 0.17). Conclusion: Based on the data obtained from our study, laparoscopic inguinal hernia repair in elderly patients was found to offer advantages such as faster recovery, reduced postoperative and chronic pain, without an increase in complications. Given these benefits, laparoscopic hernia repair can be considered a safe and preferable approach for elderly patients. Trial registration: Clinical trials number: NCT06417346.

Background/Aims: Hepatocellular carcinoma (HCC) is a prevalent cancer that significantly contributes to mortality globally, primarily due to its late diagnosis. Early detection is crucial yet challenging. This study leverages the potential of deep learning (DL) technologies, employing the You Only Look Once (YOLO) architecture, to enhance the detection of HCC in computed tomography (CT) images, aiming to improve early diagnosis and thereby patient outcomes. Materials and methods: We used a dataset of 1290 CT images from 122 patients, segmented according to a standard 70:20:10 split for training, validation, and testing phases. The YOLO-based DL model was trained on these images, with subsequent phases for validation and testing to assess the model’s diagnostic capabilities comprehensively. Results: The model exhibited exceptional diagnostic accuracy, with a precision of 0.97216, recall of 0.919, and an overall accuracy of 95.35%, significantly surpassing traditional diagnostic approaches. It achieved a specificity of 95.83% and a sensitivity of 94.74%, evidencing its effectiveness in clinical settings and its potential to reduce the rate of missed diagnoses and unnecessary interventions. Conclusion: The implementation of the YOLO architecture for detecting HCC in CT scans has shown substantial promise, indicating that DL models could soon become a standard tool in oncological diagnostics. As artificial intelligence technology continues to evolve, its integration into healthcare systems is expected to advance the accuracy and efficiency of diagnostics in oncology, enhancing early detection and treatment strategies and potentially improving patient survival rates. Cite this article as: Şahin E, O, Tatar C, et al. Diagnostic performance of deep learning applications in hepatocellular carcinoma detection using computed tomography imaging. Turk J Gastroenterol. 2025;36(2):124-130.