# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.21. Deep Abdominal Net
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Authors** & **Method** & **Purpose and Used Data** & **Evaluation Metrics** \\ \hline
2018 & [PERSON] et al. (2012) & DLS & Staging liver fibrosis; 7461 patients with confirmed liver fibrosis & AUROC scores: 0.96, 0.97, 0.95 for diagnosing significant fibrosis, advanced fibrosis, and cirrhosis, respectively; accuracy: 74.9 \\ \hline
2018 & [PERSON] et al. (2013) & DCNN & Staging liver fibrosis; 496 CT scans from 286 patients & AUROC scores: 0.74, 0.76, and 0.73 for diagnosing significant fibrosis, advanced fibrosis, and cirrhosis, respectively \\ \hline
2018 & [PERSON] et al. (2014) & UNet++ & Liver segmentation; 331 Samples & IoU of 3.9 and 3.4 points compared to U-Net and wide U-Net, respectively \\ \hline
2018 & [PERSON] et al. (2015) & Deep V-Networks & Pancreas, stomach, and esophagus segmentation for endoscopic procedures; 90 subjects & Dice scores: pancreas 0.78, stomach 0.90, esophagus 0.76 \\ \hline
2019 & [PERSON] et al. (2016) & FCN \& cGAN & Liver lesion; 60 PET/CT scans & 28\% false positive reduction \\ \hline
2020 & [PERSON] et al. (2017) & UNET3+ & Liver/spleen segmentation; 131 Samples, 103 for train, 28 for test & Dice scores 0.9675 and 0.9620 for liver/spleen, respectively \\ \hline
2020 & [PERSON] et al. (2018) & Multiscale DL & Blunt hepatic injury; 130 samples, 113 blunt, 17 penetrating & Dice score of 0.61 \\ \hline
2020 & [PERSON] et al. (2019) & DLA & Segmentation of liver/spleen; 813 patients and evaluated 150/50 pairs of CT & Dice scores: liver 0.973; spleen 0.974 \\ \hline
2020 & [PERSON] et al. (2020) & RA-UNet & 3D liver/tumor segmentation; LiTS and 3 DIRCADb datasets & Dice score of 0.595 and 0.830 for tumor segmentation \\ \hline
2020 & [PERSON] et al. (2021) & Encoder-Decoder Networks & Segmentation at L3 verteba level; 1748 CT images & Jaccard scores: 97/\% for SM and VAT and 98\% and 83\% for SAT and IMAT tissue segmentation \\ \hline
2021 & [PERSON] et al. (2021) & S-Net & Liver tumor segmentation; LiTS & Dice scores: global 75.5\%; per case 61.3\% \\ \hline
2021 & [PERSON] et al. (2023) & Conditional GAN & Liver, kidneys, and spleen segmentation from abdominal CT; CHAOS & Dice score 97.95,89.67,90.56,84.70\%, respectively, for liver, spleen, right kidney, and left kidney segmentation \\ \hline
2021 & [PERSON] & & Lesion detection and segmentation; 11S & Dice score: 82.99\%, MCC: 83.62\% \\  & [PERSON] et al. (2024) & RetinaNet and UNet & Lesion detection and segmentation; LiTS & Dice score: 82.99\%, MCC: 83.62\% \\ \hline
2021 & [PERSON] et al. (2025) & Multiscale DL \& SVM & Quantification of traumatic hemoperitoneum; total patients: 130, diagnosis: traumatic hemoperitoneum & Dice Score: 0.61\% \\  & & & & \\ \hline
2021 & [PERSON] et al. (2026) & RCN & Multi-organ registration on 3D abdominal CT images; LiTS, 3 DIRCADB, BTCV, Silver 07 & Dice score 97.75\% for multi-organ segmentation \\ \hline
2022 & [PERSON] et al. (2027) & CNN & Quantitative assessment of liver trauma; 77 CT scans (34 with and 43 without liver parenchymal trauma) & Dice score 96.31\% and 51.21\% for liver parenchyma and liver trauma, respectively. \\ \hline
2023 & [PERSON] et al. (2028) & Swin-Unet & Segmentation of the aorta, gallbladder, spleen, left kidney, right kidney, liver, pancreas, and stomach; 3779 CT images from 30 patient includes 18 train and 12 test \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chronological summary of deep learning studies in medical imaging (2018–2024).