Application of 3D reconstruction through CT to measure the abdominal cavity volume in the treatment of external abdominal hernia

Comparison of two-dimensional measurement with three-dimensional volume rendering for the assessment of loss of domain in incisional hernia patients

PURPOSE

We aimed to compare simple two-dimensional (2D) measurement with comprehensive three-dimensional (3D) volume rendering to determine loss of domain (LOD), a clinically important decision-making feature for incisional hernia repair.

METHODS

In this single-center retrospective study, we analyzed the CT scans of a consecutive cohort of adult patients with a midline incisional hernia. The hernia sac- and abdominal cavity volumes were obtained by two different methods. The 2D method estimated the volumes using the corresponding height, width, and depth. The 3D method comprised of a volume rendering tool. For both methods, LOD was calculated according to the Sabbagh ratio (hernia sac volume / (hernia sac volume + abdominal cavity volume)). Taking the 3D method as the reference standard, the performance of the 2D method was expressed as positive predictive value (PPV) and negative predictive value (NPV) for LOD of more than- and less than 20%. The agreement between both methods was expressed as Cohen's kappa coefficient (kappa).

RESULTS

We analyzed 92 CT scans. Agreement between both methods was high (kappa = 0.854, p = 0.0001); all 67 measurements for which the 2D method assessed LOD to be less than 20% were correctly classified (NPV = 100%), and 20 of 25 measurements for which the 2D method assessed LOD to be more than 20% were correctly classified (PPV = 80%).

CONCLUSIONS

The 2D method can exclude patients from perioperative actions needed for a more complex hernia. Since this method is easy to use and less time-consuming, it seems useful for the routine radiological assessment of LOD in clinical practice.

Logistic regression analysis of risk factors for intra-abdominal hypertension after giant ventral hernia repair: a retrospective cohort study

BACKGROUND

Intra-abdominal hypertension (IAH) is a classical complication after giant ventral hernia surgery and may lead to abdominal compartment syndrome (ACS). Assessment of risk factors and prevention of IAH/ACS are essential for hernia surgeons.

METHODS

We performed a retrospective study including 58 giant ventral hernia patients in our center between Jan 1, 2017, and Mar 1, 2022, we recorded age, gender, chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), hypertension, type 2 diabetes mellitus (T2DM), hypoproteinemia, body mass index (BMI), the ratio of hernia sac volume to abdominal cavity volume (HSV/ACV), defect width, tension reduction procedure (TRP), positive fluid balance (PFB) and IAH of these patients and analyzed the data using univariate and multivariate logistic regression to screen the risk factors for IAH after surgery.

RESULTS

The multivariate analysis showed that HSV/ACV ≥ 25%, hypoproteinemia, and PFB were independent risk factors for the occurrence of IAH after giant ventral hernia repair (P = 0.025, 0.016, 0.017, respectively). We did not find any correlation between postoperative IAH and the patient's age, gender, COPD, CHD, hypertension, T2DM, BMI, defect width, TRP, and PFB.

CONCLUSION

Identifying risk factors is of great significance for the early identification and prevention of IAH/ACS. We found that HSV/ACV ≥ 25%, hypoproteinemia, and PFB were independent risk factors for IAH after giant ventral hernia repair.

Effect Evaluation of Perioperative Fast-Track Surgery Nursing for Tibial Fracture Patients with Computerized Tomography Images under Intelligent Algorithm

This study aimed to study the application value of computerized tomography (CT) images under the graph cut algorithm in the effect evaluation of perioperative fast-track surgery (FTS) nursing in tibial fracture. In this study, 80 tibial fracture patients in the perioperative period were selected as the research objects. These objects were randomly divided into two groups according to the examination method. In group A, routine CT examination was performed; in group B, CT examination under the graph cut algorithm was applied. The imaging results showed that there were still 16 cases with collapse of group A and 34 cases with collapse of group B; the difference was statistically significant (P < 0.05). As for 16 cases with collapse in both groups, the average collapse shown in group A was about 2.79 ± 1.31 mm, while that in group B was 5.51 ± 1.88 mm, with a statistically significant difference (P < 0.05). The average broadening in the images of group A was 3.17 ± 1.41 mm and that of group B was 5.72 ± 1.83 mm, suggesting that the difference was statistically significant (P < 0.05). The broadening distance of 3-4 mm was mainly shown in the images of group A and that of 5-8 mm was shown in group B, with a statistical difference (P < 0.05). In terms of the total score, there were 26, 44, 8, and 2 cases that were assessed as excellent, good, common, and bad, respectively, in group A, while 44 cases were assessed as good and 36 cases were assessed as common in group B, which were significantly different (P < 0.05). In summary, the graph cut algorithm not only had a good segmentation effect and segmentation efficiency but also could improve the evaluation of CT images for perioperative FTS nursing effect in patients with tibial fracture.

Artificial Intelligence-Based Spiral CT 3D Reconstruction in Transcatheter Aortic Valve Implantation

To evaluate the clinical application effect of spiral computed tomography (CT) three-dimensional (3D) reconstruction based on artificial intelligence in transcatheter aortic valve implantation (TAVI), a CT 3D reconstruction model based on deep convolutional neural networks (DCNN) was established in this research, which was compared with the model-based iterative reconstruction (MBIR) and used in clinical practice. Then, 62 patients with aortic stenosis (AS) who underwent TAVI surgery were recruited as the research objects. The accuracy, sensitivity, and specificity of the multislice spiral CT scan (MSCT) and transthoracic echocardiography (TTE) in predicting the type of TAVI surgical valve were compared and analyzed. The results showed that the mean absolute error (MAE) (0.01) and root mean square error (RMSE) (0.086) of the MBIR model were higher than the reconstruction model in this research. The structural similarity (SSIM) (0.831) and peak signal-to noise ratio (PSNR) (32.77 dB) of the MBIR model were lower than the reconstruction model, and the differences were considerable ( $P<0.05$ ). Of the valve models selected based on the TTE measurement results, 35 cases were accurately predicted and 27 cases were incorrectly predicted. The accuracy of MSCT was 87.1%, the specificity was 98.84%, and the sensitivity was 92.87%; all of which were significantly higher than TTE ( $P<0.05$ ). In summary, compared with the MBIR reconstruction model, the imaging results of the model established in this research were closer to the real image. Compared with TTE, MSCT had higher accuracy, sensitivity, and specificity and can provide more accurate preoperative predictions for patients undergoing TAVI surgery.