3D auto-segmentation of biliary structure of living liver donors using magnetic resonance cholangiopancreatography for enhanced preoperative planning.

BACKGROUND: This study aimed to develop an automated segmentation system for biliary structures using a deep learning model, based on data from magnetic resonance cholangiopancreatography (MRCP). MATERIALS AND METHODS: Living liver donors who underwent MRCP using the gradient and spin echo technique followed by three-dimensional modeling were eligible for this study. A three-dimensional residual U-Net model was implemented for the deep learning process. Data were divided into training and test sets at a 9:1 ratio. Performance was assessed using the dice similarity coefficient to compare the model's segmentation with the manually labeled ground truth. RESULTS: The study incorporated 250 cases. There was no difference in the baseline characteristics between the train set (n=225) and test set (n=25). The overall mean Dice Similarity Coefficient was 0.80±0.20 between the ground truth and inference result. The qualitative assessment of the model showed relatively high accuracy especially for the common bile duct (88%), common hepatic duct (92%), hilum (96%), right hepatic duct (100%), and left hepatic duct (96%), while the third-order branch of the right hepatic duct (18.2%) showed low accuracy. CONCLUSION: The developed automated segmentation model for biliary structures, utilizing MRCP data and deep learning techniques, demonstrated robust performance and holds potential for further advancements in automation.

CoFe2O4 on Mica Substrate as Flexible Ethanol Gas Sensor in Self-Heating Mode.

In this study, a novel flexible ethanol gas sensor was created by the deposition of a CoFe2O4 (CFO) thin film on a thin mica substrate using the pulsed laser deposition technique. Transition electron microscopy (TEM) investigations clearly demonstrated the successful growth of CFO on the mica, where a well-defined interface was observed. Ethanol gas-sensing studies showed optimal performance at 200 °C, with the highest response of 19.2 to 100 ppm ethanol. Operating the sensor in self-heating mode under 7 V applied voltage, which corresponds to a temperature of approximately 200 °C, produced a maximal response of 19.2 to 100 ppm ethanol. This aligned with the highest responses observed during testing at 200 °C, confirming the sensor's accuracy and sensitivity to ethanol under self-heating conditions. In addition, the sensor exhibited good selectivity to ethanol and excellent flexibility, maintaining its high performance after bending and tilting up to 5000 times. As this is the first report on flexible self-heated CFO gas sensors, we believe that this research holds great promise for the future development of high-quality sensors based on this approach.

Selective Spin Dewetting for Perovskite Solar Modules Fabricated on Engineered Au/ITO Substrates.

We introduce a novel method for fabricating perovskite solar modules using selective spin-coating on various Au/ITO patterned substrates. These patterns were engineered for two purposes: (1) to enhance selectivity of monolayers primarily self-assembling on the Au electrode, and (2) to enable seamless interconnection between cells through direct contact of the top electrode and the hydrophobic Au connection electrode. Utilizing SAMs-treated Au/ITO, we achieved sequential selective deposition of the electron transport layer (ETL) and the perovskite layer on the hydrophilic amino-terminated ITO, while the hole transport layer (HTL) was deposited on the hydrophobic CH3-terminated Au connection electrodes. Importantly, our approach had a negligible impact on the series resistance of the solar cells, as evidenced by the measured specific contact resistivity of the multilayers. A significant outcome was the production of a six-cell series-connected solar module with a notable average PCE of 8.32%, providing a viable alternative to the conventional laser scribing technique.

nnU-Net-Based Pancreas Segmentation and Volume Measurement on CT Imaging in Patients with Pancreatic Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning (DL)-based method for pancreas segmentation on CT and automatic measurement of pancreatic volume in pancreatic cancer. MATERIALS AND METHODS: This retrospective study used 3D nnU-net architecture for fully automated pancreatic segmentation in patients with pancreatic cancer. The study used 851 portal venous phase CT images (499 pancreatic cancer and 352 normal pancreas). This dataset was divided into training (n = 506), internal validation (n = 126), and external test set (n = 219). For the external test set, the pancreas was manually segmented by two abdominal radiologists (R1 and R2) to obtain the ground truth. In addition, the consensus segmentation was obtained using Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. Segmentation performance was assessed using the Dice similarity coefficient (DSC). Next, the pancreatic volumes determined by automatic segmentation were compared to those determined by manual segmentation by two radiologists. RESULTS: The DL-based model for pancreatic segmentation showed a mean DSC of 0.764 in the internal validation dataset and DSC of 0.807, 0.805, and 0.803 using R1, R2, and STAPLE as references in the external test dataset. The pancreas parenchymal volume measured by automatic and manual segmentations were similar (DL-based model: 65.5 ± 19.3 cm3 and STAPLE: 65.1 ± 21.4 cm3; p = 0.486). The pancreatic parenchymal volume difference between the DL-based model predictions and the manual segmentation by STAPLE was 0.5 cm3, with correlation coefficients of 0.88. CONCLUSION: The DL-based model efficiently generates automatic segmentation of the pancreas and measures the pancreatic volume in patients with pancreatic cancer.

Radiation shielding effects of lead equivalent thickness of a radiation protective apron and distance during C-arm fluoroscopy-guided pain interventions: A randomized trial.

BACKGROUND: The present study aimed to evaluate the degree of radiation shielding effects according to lead equivalent thickness and distance during C-arm fluoroscopy-guided lumbar interventions. METHODS: The exposure time and air kerma were recorded using a fluoroscope. The effective dose (ED) was measured with and without the shielding material of the lead apron using 2 dosimeters at 2 positions. According to the lead equivalent thickness of the shielding material and distance from the side of the table, the groups were divided into 4 groups: group 1 (lead equivalent thickness 0.6 mm, distance 0 cm), group 2 (lead equivalent thickness 0.6 mm, distance 5 cm), group 3 (lead equivalent thickness 0.3 mm, distance 0 cm), and group 4 (lead equivalent thickness 0.3 mm, distance 5 cm). Mean differences such as air kerma, exposure time, ED, and ratio of EDs (ED with protector/ED without protector) were analyzed. RESULTS: A total of 400 cases (100 cases in each group) were collected. The ratio of ED was significantly lower in groups 1 and 2 (9.18 ±â€…2.78% and 9.56 ±â€…3.29%, respectively) when compared to that of groups 3 and 4 (21.93 ±â€…4.19% and 21.53 ±â€…4.30%, respectively). The reductive effect of a 5-cm distance was 33.3% to 36.1% when comparing the ED between groups 1 and 2 and groups 3 and 4. CONCLUSIONS: The 0.3- and 0.6-mm lead equivalent thickness protectors have a radiation attenuation effect of 78.1% to 78.5% and 90.4% to 90.8%, respectively. The 5-cm distance from the side of the table reduces radiation exposure by 33.3% to 36.1%.

Blunt Trauma in Children: Efficacy and Safety of Transarterial Embolization, 10-Year Experiences in a Single Trauma Center.

BACKGROUND: Transcatheter arterial embolization (TAE) is an established approach for controlling hemorrhage in adults with acute abdominal and pelvic trauma. However, its application in pediatric trauma is not well established. This study aimed to evaluate the safety and effectiveness of TAE in a population of pediatric patients with blunt trauma. METHODS: This retrospective study was conducted in pediatric patients (<18 years) who underwent TAE for blunt trauma between February 2014 and July 2022. The patients were categorized into subgroups based on age and body weight. Patient demographics, injury severity, transfusion requirements, and clinical outcomes were analyzed. RESULTS: Exactly 73 patients underwent TAE. Technical success was achieved in all patients (100%), and clinical success was achieved in 83.6%. The mortality and complication rates were 4.1% and 1.4%, respectively. The mean duration of hospitalization was 19.3 days. Subgroup analysis showed that age, body weight, and sex did not significantly affect clinical success. The injury severity score and transfusion requirement were predictors of clinical success, with lower values associated with better outcomes. CONCLUSIONS: TAE is effective and safe for managing blunt pediatric trauma in younger and lighter patients. Injury severity and transfusion requirement are predictors of clinical success.

Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning.

Recent advancements in deep learning have facilitated significant progress in medical image analysis. However, there is lack of studies specifically addressing the needs of surgeons in terms of practicality and precision for surgical planning. Accurate understanding of anatomical structures, such as the liver and its intrahepatic structures, is crucial for preoperative planning from a surgeon's standpoint. This study proposes a deep learning model for automatic segmentation of liver parenchyma, vascular and biliary structures, and tumor mass in hepatobiliary phase liver MRI to improve preoperative planning and enhance patient outcomes. A total of 120 adult patients who underwent liver resection due to hepatic mass and had preoperative gadoxetic acid-enhanced MRI were included in the study. A 3D residual U-Net model was developed for automatic segmentation of liver parenchyma, tumor mass, hepatic vein (HV), portal vein (PV), and bile duct (BD). The model's performance was assessed using Dice similarity coefficient (DSC) by comparing the results with manually delineated structures. The model achieved high accuracy in segmenting liver parenchyma (DSC 0.92 ± 0.03), tumor mass (DSC 0.77 ± 0.21), hepatic vein (DSC 0.70 ± 0.05), portal vein (DSC 0.61 ± 0.03), and bile duct (DSC 0.58 ± 0.15). The study demonstrated the potential of the 3D residual U-Net model to provide a comprehensive understanding of liver anatomy and tumors for preoperative planning, potentially leading to improved surgical outcomes and increased patient safety.

Peptide Nucleic Acids Containing Cationic/Amino-Alkyl Modified Bases Promote Enhanced Hybridization Kinetics and Thermodynamics with Single-Strand DNA.

Peptide nucleic acids (PNAs) are antisense molecules with excellent polynucleotide hybridization properties; they are resistant to nuclease degradation but often have poor cell permeability leading to moderate cellular activity and limited clinical results. The addition of cationic substitutions (positive charges) to PNA molecules greatly increases cell permeability. In this report, we describe the synthesis and polynucleotide hybridization properties of a novel cationic/amino-alkyl nucleotide base-modified PNA (OPNA). This study was designed to quantitate the effect the cationic/amino-alkyl nucleotide base modification had on the kinetic and thermodynamic properties of OPNA-DNA hybridization using surface plasmon resonance and UV thermal melt studies. Kinetic studies reveal a favorable 10-30 fold increase in affinity for a single cationic modification on the base of an adenine, cytosine, or guanidine OPNA sequence compared to the nonmodified PNA strand. The increase in affinity is correlated directly with a favorable decrease in the dissociation rate constant and increase in the association rate constant. Introducing additional amino-alkyl base modifications further favors a decrease in the dissociation rate (3-10-fold per amino-alkyl). The thermodynamics driving the OPNA hybridization is promoted by an additional favorable -80 kJ/mol enthalpy of binding for a single amino-alkyl modification compared to the PNA strand. This increase in enthalpy is consistent with an ion-ion interaction with the DNA strand. These kinetic and thermodynamic hybridization studies reveal for the first time that this type of cationic/amino-alkyl base-modified PNA has favorable hybridization properties suitable for development as an antisense oligomer.

Real-time morphological detection of label-free submicron-sized plastics using flow-channeled differential interference contrast microscopy.

Owing to the surge in plastic waste generated during the COVID-19 pandemic, concerns regarding microplastic pollution in aqueous environments are increasing. Since microplastics (MPs) are broken down into submicron (< 1 µm) and nanoscale plastics, their real-time morphological detection in water is necessary. However, the decrease in the scattering cross-section of MPs in aqueous media precludes morphological detection by bright-field microscopy. To address this problem, we propose and demonstrate a differential interference contrast (DIC) system that incorporates a magnification-enhancing system to detect MPs in aqueous samples. To detect MPs in both the stationary and mobile phases, a microfluidic chip was designed, taking into consideration the imaging depth of focus and flow resistance. MPs of various sizes flowing in deionized, tap, and pond water at varying speeds were observed under Static and Flow conditions. Successful real-time morphological detection and quantification of polystyrene beads down to 200 nm at a constant flow rate in water were achieved. Thus, the proposed novel method can significantly reduce analysis time and improve the size-detection limit. The proposed DIC microscopy system can be coupled with Raman or infrared spectroscopy in future studies for chemical composition analysis. ENVIRONMENTAL IMPLICATION: Microplastics (MPs), particularly submicron plastics < 1-µm, can pose a risk to human health and aquatic ecosystems. Existing methods for detecting MPs in the aqueous phase have several limitations, including the use of expensive instruments and prolonged and labor-intensive procedures. Our results clearly demonstrated that a new low-cost flow-channeled differential interference contrast microscopy enables the real-time morphological detection and quantification of MPs down to 200 nm under flowing conditions without sample labeling. Consequently, our proposed rapid method for accurate quantitative measurements can serve as a valuable reference for detecting submicron plastics in water samples.

Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI.

BACKGROUND: the objective of this study is to evaluate the predictive power of the survival model using deep learning of diffusion-weighted images (DWI) in patients with non-small-cell lung cancer (NSCLC). METHODS: DWI at b-values of 0, 100, and 700 sec/mm2 (DWI0, DWI100, DWI700) were preoperatively obtained for 100 NSCLC patients who underwent curative surgery (57 men, 43 women; mean age, 62 years). The ADC0-100 (perfusion-sensitive ADC), ADC100-700 (perfusion-insensitive ADC), ADC0-100-700, and demographic features were collected as input data and 5-year survival was collected as output data. Our survival model adopted transfer learning from a pre-trained VGG-16 network, whereby the softmax layer was replaced with the binary classification layer for the prediction of 5-year survival. Three channels of input data were selected in combination out of DWIs and ADC images and their accuracies and AUCs were compared for the best performance during 10-fold cross validation. RESULTS: 66 patients survived, and 34 patients died. The predictive performance was the best in the following combination: DWI0-ADC0-100-ADC0-100-700 (accuracy: 92%; AUC: 0.904). This was followed by DWI0-DWI700-ADC0-100-700, DWI0-DWI100-DWI700, and DWI0-DWI0-DWI0 (accuracy: 91%, 81%, 76%; AUC: 0.889, 0.763, 0.711, respectively). Survival prediction models trained with ADC performed significantly better than the one trained with DWI only (p-values < 0.05). The survival prediction was improved when demographic features were added to the model with only DWIs, but the benefit of clinical information was not prominent when added to the best performing model using both DWI and ADC. CONCLUSIONS: Deep learning may play a role in the survival prediction of lung cancer. The performance of learning can be enhanced by inputting precedented, proven functional parameters of the ADC instead of the original data of DWIs only.

Eye-Mimicked Neural Network Composed of Photosensitive Neural Spheroids with Human Opsin Proteins.

An in vitro model, composed of the short-wavelength human opsins and rhodopsins, is created. Two types of photosensitive neural spheroids are transfected for selective reaction under bluish-purple and green lights. These are employed to two devices with intact neuron and neural-spheroid to study the interaction. By photostimulation, the photosensitive spheroid initiated photoactivation, and the signal generated from its body is transmitted to adjacent neural networks. Specifically, the signal traveled through the axon bundle in narrow gap from photosensitive spheroid to intact spheroid as an eye-to-brain model including optic nerve. The whole process with photosensitive spheroid is monitored by calcium ion detecting fluorescence images. The results of this study can be applied to examine vision restoration and novel photosensitive biological systems with spectral sensitivity.

Multi-scale vascularization strategy for 3D-bioprinted tissue using coaxial core-shell pre-set extrusion bioprinting and biochemical factors.

Three-dimensional bioprinting is a key technology in bioartificial organ production. However, production of bioartificial organs has significant limitations because it is hard to build vascular structures, especially capillaries, in printed tissue owing to its low resolution. As the vascular structure plays a critical role in delivering oxygen and nutrients to cells and removing metabolic waste, building vascular channels in bioprinted tissue is essential for bioartificial organ production. In this study, we demonstrated an advanced strategy for fabricating multi-scale vascularized tissue using a pre-set extrusion bioprinting technique and endothelial sprouting. Using a coaxial precursor cartridge, mid-scale vasculature-embedded tissue was successfully fabricated. Furthermore, upon generating a biochemical gradient environment in the bioprinted tissue, capillaries were formed in this tissue. In conclusion, this strategy for multi-scale vascularization in bioprinted tissue is a promising technology for bioartificial organ production.

The Profile of Early Sedation Depth and Clinical Outcomes of Mechanically Ventilated Patients in Korea.

BACKGROUND: Current international guidelines recommend against deep sedation as it is associated with worse outcomes in the intensive care unit (ICU). However, in Korea the prevalence of deep sedation and its impact on patients in the ICU are not well known. METHODS: From April 2020 to July 2021, a multicenter, prospective, longitudinal, noninterventional cohort study was performed in 20 Korean ICUs. Sedation depth extent was divided into light and deep using a mean Richmond Agitation-Sedation Scale value within the first 48 hours. Propensity score matching was used to balance covariables; the outcomes were compared between the two groups. RESULTS: Overall, 631 patients (418 [66.2%] and 213 [33.8%] in the deep and light sedation groups, respectively) were included. Mortality rates were 14.1% and 8.4% in the deep and light sedation groups (P = 0.039), respectively. Kaplan-Meier estimates showed that time to extubation (P < 0.001), ICU length of stay (P = 0.005), and death (P = 0.041) differed between the groups. After adjusting for confounders, early deep sedation was only associated with delayed time to extubation (hazard ratio [HR], 0.66; 95% confidence interval [CI], 0.55-0.80; P < 0.001). In the matched cohort, deep sedation remained significantly associated with delayed time to extubation (HR, 0.68; 95% CI, 0.56-0.83; P < 0.001) but was not associated with ICU length of stay (HR, 0.94; 95% CI, 0.79-1.13; P = 0.500) and in-hospital mortality (HR, 1.19; 95% CI, 0.65-2.17; P = 0.582). CONCLUSION: In many Korean ICUs, early deep sedation was highly prevalent in mechanically ventilated patients and was associated with delayed extubation, but not prolonged ICU stay or in-hospital death.

Layer-dependent stability of 2D mica nanosheets.

We report on the layer-dependent stability of muscovite-type two-dimensional (2D) mica nanosheets (KAl3Si3O10(OH)2). First-principles calculations on mica nanosheets with different layer thicknesses (n = 1, 2, and 3) reveal their layer-dependent stability; odd-numbered 2D mica nanosheets are more stable than even-numbered ones, and the preferable stability of odd-numbered layers originates from electronic effects. A core-shielding model is proposed with a reasonable assumption, successfully proving the instability of the even-numbered mica nanosheets. Raman imaging supports that the population of odd-numbered mica nanosheets is predominant in exfoliated mica products. The alternating charge states with odd/even layers were evidenced by Kelvin probe force microscopy. We also demonstrate a unique photocatalytic degradation, opening new doors for environmental applications of mica nanosheets.

The role of AI in prostate MRI quality and interpretation: Opportunities and challenges.

Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.

Multiscale stiffness of human emphysematous precision cut lung slices.

Emphysema is a debilitating disease that remodels the lung leading to reduced tissue stiffness. Thus, understanding emphysema progression requires assessing lung stiffness at both the tissue and alveolar scales. Here, we introduce an approach to determine multiscale tissue stiffness and apply it to precision-cut lung slices (PCLS). First, we established a framework for measuring stiffness of thin, disk-like samples. We then designed a device to verify this concept and validated its measuring capabilities using known samples. Next, we compared healthy and emphysematous human PCLS and found that the latter was 50% softer. Through computational network modeling, we discovered that this reduced macroscopic tissue stiffness was due to both microscopic septal wall remodeling and structural deterioration. Lastly, through protein expression profiling, we identified a wide spectrum of enzymes that can drive septal wall remodeling, which, together with mechanical forces, lead to rupture and structural deterioration of the emphysematous lung parenchyma.

Structural and functional insights into Spns2-mediated transport of sphingosine-1-phosphate.

Sphingosine-1-phosphate (S1P) is an important signaling sphingolipid that regulates the immune system, angiogenesis, auditory function, and epithelial and endothelial barrier integrity. Spinster homolog 2 (Spns2) is an S1P transporter that exports S1P to initiate lipid signaling cascades. Modulating Spns2 activity can be beneficial in treatments of cancer, inflammation, and immune diseases. However, the transport mechanism of Spns2 and its inhibition remain unclear. Here, we present six cryo-EM structures of human Spns2 in lipid nanodiscs, including two functionally relevant intermediate conformations that link the inward- and outward-facing states, to reveal the structural basis of the S1P transport cycle. Functional analyses suggest that Spns2 exports S1P via facilitated diffusion, a mechanism distinct from other MFS lipid transporters. Finally, we show that the Spns2 inhibitor 16d attenuates the transport activity by locking Spns2 in the inward-facing state. Our work sheds light on Spns2-mediated S1P transport and aids the development of advanced Spns2 inhibitors.

Prediction of respiratory complications by quantifying lung contusion volume using chest computed tomography in patients with chest trauma.

Pulmonary contusion is an important risk factor for respiratory complications in trauma patients. Hence, we aimed to determine the relationship between the ratio of pulmonary contusion volume to the total lung volume and patient outcomes and the predictability of respiratory complications. We retrospectively included 73 patients with a pulmonary contusion on chest computed tomography (CT) from 800 patients with chest trauma admitted to our facility between January 2019 and January 2020. Chest injury severity was expressed as the ratio of pulmonary contusion volume to total lung volume by quantifying pulmonary contusion volume on chest CT. The cut-off value was 80%. Among the 73 patients with pulmonary contusion (77% males, mean age: 45.3 years), 28 patients had pneumonia, and five had acute respiratory distress syndrome. The number of patients in the severe risk group with > 20% of pulmonary contusion volume was 38, among whom 23 had pneumonia. For predicting pneumonia, the area under the receiver operating characteristic curves for the ratio of pulmonary contusion volume was 0.85 (95% confidence interval 0.76-0.95, p = 0.008); the optimal threshold was 70.4%. Quantifying pulmonary contusion volume using initial CT enables identifying patients with chest trauma at high risk of delayed respiratory complications.

A Pilot Study of Autonomic Function Screening Tests for Differentiating Complex Regional Pain Syndrome Type II and Traumatic Neuropathic Pain.

Background and Objectives: One of the most challenging tasks in a clinical setting is to differentiate between complex regional pain syndrome (CRPS) type II and traumatic neuropathic pain (NeP). CRPS is characterized by several dysautonomic manifestations, such as edema, hyper/hypohidrosis, skin color change, and tachycardia. This study compared the outcomes of autonomic function screening tests in patients with CRPS type II and traumatic NeP for diagnostic differentiation. Materials and Methods: CRPS type II was diagnosed according to the Budapest research criteria, while NeP was diagnosed according to the updated grading system suggested by the International Association for the Study of Pain Special Interest Group on Neuropathic Pain in 2016. Twenty patients with CRPS type II and twenty-five with traumatic NeP were investigated. Results: Twelve patients with CRPS type II presented abnormal results for the quantitative sudomotor axon reflex test (QSART). Abnormal QSART results were more common in the CRPS type II group. Conclusions: Analysis of QSART combined with other ancillary tests can help in the differential diagnosis of CRPS type II and traumatic NeP if factors influencing abnormal QSART are sufficiently controlled.

Complications and recovery patterns after blunt splenic injury: Recommended duration and follow-up methods.

BACKGROUND: Splenic artery embolization (SAE) is commonly employed as a non-operative management technique for splenic injury. Nonetheless, information on follow-up duration and methods, and the natural course of splenic infarction after SAE is limited. Thus, this study is aimed to analyze the patterns of complications and recovery of splenic infarction after SAE and to determine the appropriate follow-up duration and method. METHODS: Medical records of 314 patients with blunt splenic injury admitted at the Pusan National University Hospital, Level I Trauma Centre were analyzed to identify patients who underwent SAE between January 2014 and November 2018. Computed tomography (CT) scans that were obtained after SAE in patients who were followed up were compared with all their previous CT scans to identify any changes in the spleen and the occurrence of complications such as sustained bleeding, pseudoaneurysm, splenic infarctions, or abscess formation. RESULTS: Of the 314 patients, 132 who underwent SAE were included in the study. In total, 30 complications were noted among the 132 patients; of these, 7 (5.30%) required repeat embolization and 9 (6.82%) required splenectomy. Splenic infarction of <50% occurred in 76 patients and that of ≥50% including total and near-total infarctions occurred in 40 patients. Among patients with splenic infarction of ≥50%, 3 (2.27%) patients had abscesses between 16 and 21 days after SAE, and the range of infarctions increased as the AAAST-OIS grade increased. After SAE, repeat abdominal CT scans for >14 days were obtained in 75 patients; among these, 67 pre-sented with recovery of splenic infarction. The median period of recovery was 43 days after SAE. CONCLUSION: The present findings suggest that patients with ≥50% infarction may need 3 weeks of closed observation, with or without a follow-up CT scan, to rule out infection after SAE, follow-up CT follow-up at 6 weeks after SAE may be necessary to confirm the recovery of the spleen.