Small bowel motility can be quantified using cine MRI, but the influence of patient and imaging factors on motility scores remains unclear. This study evaluated whether patient and imaging factors affect motility scores derived from deep learning-based segmentation of cine MRI. Fifty-four patients (mean age 53.6 ± 16.4 years; 34 women) with chronic constipation or suspected colonic pseudo-obstruction who underwent cine MRI covering the entire small bowel between 2022 and 2023 were included. A deep learning algorithm was developed to segment small bowel regions, and motility was quantified with an optical flow-based algorithm, producing a motility score for each slice. Associations of motility scores with patient factors (age, sex, body mass index, symptoms, and bowel distension) and MRI slice-related factors (anatomical location, bowel area, and anteroposterior position) were analyzed using linear mixed models. Deep learning-based small bowel segmentation achieved a mean volumetric Dice similarity coefficient of 75.4 ± 18.9%, with a manual correction time of 26.5 ± 13.5 s. Median motility scores per patient ranged from 26.4 to 64.4, with an interquartile range of 3.1-26.6. Multivariable analysis revealed that MRI slice-related factors, including anatomical location with mixed ileum and jejunum (β = -4.9; p = 0.01, compared with ileum dominant), bowel area (first order β = -0.2, p < 0.001; second order β = 5.7 × 10^-4, p < 0.001), and anteroposterior position (first order β = -51.5, p < 0.001; second order β = 28.8, p = 0.004) were significantly associated with motility scores. Patient factors showed no association with motility scores. Small bowel motility scores were significantly associated with MRI slice-related factors. Determining global motility without adjusting for these factors may be limited. Question Global small bowel motility can be quantified from cine MRI; however, the confounding factors affecting motility scores remain unclear. Findings Motility scores were significantly influenced by MRI slice-related factors, including anatomical location, bowel area, and anteroposterior position. Clinical relevance Adjusting for slice-related factors is essential for accurate interpretation of small bowel motility scores on cine MRI.

Background: Accurate lesion segmentation is critical for multiple sclerosis (MS) diagnosis, yet current deep learning approaches face robustness challenges. Aim: This study improves MS lesion segmentation by combining data fusion and deep learning techniques. Materials and Methods: We suggested novel radiomic features (concentration rate and R\'enyi entropy) to characterize different MS lesion types and fused these with raw imaging data. The study integrated radiomic features with imaging data through a ResNeXt-UNet architecture and attention-augmented U-Net architecture. Our approach was evaluated on scans from 46 patients (1102 slices), comparing performance before and after data fusion. Results: The radiomics-enhanced ResNeXt-UNet demonstrated high segmentation accuracy, achieving significant improvements in precision and sensitivity over the MRI-only baseline and a Dice score of 0.774$\pm$0.05; p<0.001 according to Bonferroni-adjusted Wilcoxon signed-rank tests. The radiomics-enhanced attention-augmented U-Net model showed a greater model stability evidenced by reduced performance variability (SDD = 0.18 $\pm$ 0.09 vs. 0.21 $\pm$ 0.06; p=0.03) and smoother validation curves with radiomics integration. Conclusion: These results validate our hypothesis that fusing radiomics with raw imaging data boosts segmentation performance and stability in state-of-the-art models.

To investigate the value of deep learning-based quantitative CT (QCT) in predicting progressive fibrosing interstitial lung disease (PF-ILD) and assessing prognosis. This single-center retrospective study included ILD patients with CT examinations between January 2015 and June 2021. Each ILD finding (ground-glass opacity (GGO), reticular opacity (RO), honeycombing) and fibrosis (sum of RO and honeycombing) was quantified from baseline and follow-up CTs. Logistic regression was performed to identify predictors of PF-ILD, defined as radiologic progression along with forced vital capacity (FVC) decline ≥ 5% predicted. Cox proportional hazard regression was used to assess mortality. The added value of incorporating QCT into FVC was evaluated using C-index. Among 465 ILD patients (median age [IQR], 65 [58-71] years; 238 men), 148 had PF-ILD. After adjusting for clinico-radiological variables, baseline RO (OR: 1.096, 95% CI: 1.042, 1.152, p < 0.001) and fibrosis extent (OR: 1.035, 95% CI: 1.004, 1.067, p = 0.025) were PF-ILD predictors. Baseline RO (HR: 1.063, 95% CI: 1.013, 1.115, p = 0.013), honeycombing (HR: 1.074, 95% CI: 1.034, 1.116, p < 0.001), and fibrosis extent (HR: 1.067, 95% CI: 1.043, 1.093, p < 0.001) predicted poor prognosis. The Cox models combining baseline percent predicted FVC with QCT (each ILD finding, C-index: 0.714, 95% CI: 0.660, 0.764; fibrosis, C-index: 0.703, 95% CI: 0.649, 0.752; both p-values < 0.001) outperformed the model without QCT (C-index: 0.545, 95% CI: 0.500, 0.599). Deep learning-based QCT for ILD findings is useful for predicting PF-ILD and its prognosis. Question Does deep learning-based CT quantification of interstitial lung disease (ILD) findings have value in predicting progressive fibrosing ILD (PF-ILD) and improving prognostication? Findings Deep learning-based CT quantification of baseline reticular opacity and fibrosis predicted the development of PF-ILD. In addition, CT quantification demonstrated value in predicting all-cause mortality. Clinical relevance Deep learning-based CT quantification of ILD findings is useful for predicting PF-ILD and its prognosis. Identifying patients at high risk of PF-ILD through CT quantification enables closer monitoring and earlier treatment initiation, which may lead to improved clinical outcomes.

Advances in treatment technology now allow for the use of customizable 3D-printed hydrogel wound dressings for patients with osteoradionecrosis (ORN) of the jaw (ONJ). Meanwhile, deep learning has enabled precise segmentation of 3D medical images using tools like nnUNet. However, the scarcity of labeled data in ONJ imaging makes supervised training impractical. This study aims to develop an unsupervised training approach for automatically identifying anomalies in imaging scans. We propose a novel two-stage training pipeline. In the first stage, a VQ-GAN is trained to accurately reconstruct normal subjects. In the second stage, random cube masking and ONJ-specific masking are applied to train a new encoder capable of recovering the data. The proposed method achieves successful segmentation on both simulated and real patient data. This approach provides a fast initial segmentation solution, reducing the burden of manual labeling. Additionally, it has the potential to be directly used for 3D printing when combined with hand-tuned post-processing.

Tractography is a challenging process with complex rules, driving continuous algorithmic evolution to address its challenges. Meanwhile, deep learning has tackled similarly difficult tasks, such as mastering the Go board game and animating sophisticated robots. Given its transformative impact in these areas, deep learning has the potential to revolutionize tractography within the framework of existing rules. This work provides a brief summary of recent advances and challenges in deep learning-based tractography computing and analysis.

The number of patients referred for and requiring a transthoracic echocardiogram (TTE) has increased over the years resulting in more cardiac sonographers reporting work related musculoskeletal pain. We sought to determine if a scanning protocol that replaced conventional workflows with advanced technologies such as multiplane imaging, artificial intelligence (AI) and automation could be used to optimise conventional workflows and potentially reduce ergonomic risk for cardiac sonographers. The aim was to assess whether this alternate protocol could reduce active scanning time as well as interaction with the ultrasound machine compared to a standard echocardiogram without a reduction in image quality and interpretability. Volunteer participants were recruited for a study that comprised of two TTE's with separate protocols. Both were clinically complete, but Protocol A combined automation, AI assisted acquisition and measurement, simultaneous and multiplane imaging whilst Protocol B reflected a standard scanning protocol without these additional technologies. Keystrokes were significantly reduced with the advanced protocol as compared to the typical protocol (230.9 ± 24.2 vs. 502.8 ± 56.2; difference 271.9 ± 61.3, p < 0.001). Furthermore, there was a reduction in scan time with protocol A compared to protocol B the standard TTE protocol (13.4 ± 2.3 min vs. 18.0 ± 2.6 min; difference 4.6 ± 2.9 min, p < 0.001) as well as a decrease of approximately 27% in the time the sonographers were required to reach beyond a neutral position on the ultrasound console. A TTE protocol that embraces modern technologies such as AI, automation, and multiplane imaging shows potential for a reduction in ultrasound keystrokes and scan time without a reduction in quality and interpretability. This may aid a reduction in ergonomic workload as compared to a standard TTE.

Inter reader variability and cross site domain shift challenge the automatic segmentation of prostate anatomy using T2 weighted MRI images. This study investigates whether transformer models can retain precision amid such heterogeneity. We compare the performance of UNETR and SwinUNETR in prostate gland segmentation against our previous 3D UNet model [1], based on 546 MRI (T2weighted) volumes annotated by two independent experts. Three training strategies were analyzed: single cohort dataset, 5 fold cross validated mixed cohort, and gland size based dataset. Hyperparameters were tuned by Optuna. The test set, from an independent population of readers, served as the evaluation endpoint (Dice Similarity Coefficient). In single reader training, SwinUNETR achieved an average dice score of 0.816 for Reader#1 and 0.860 for Reader#2, while UNETR scored 0.8 and 0.833 for Readers #1 and #2, respectively, compared to the baseline UNets 0.825 for Reader #1 and 0.851 for Reader #2. SwinUNETR had an average dice score of 0.8583 for Reader#1 and 0.867 for Reader#2 in cross-validated mixed training. For the gland size-based dataset, SwinUNETR achieved an average dice score of 0.902 for Reader#1 subset and 0.894 for Reader#2, using the five-fold mixed training strategy (Reader#1, n=53; Reader#2, n=87) at larger gland size-based subsets, where UNETR performed poorly. Our findings demonstrate that global and shifted-window self-attention effectively reduces label noise and class imbalance sensitivity, resulting in improvements in the Dice score over CNNs by up to five points while maintaining computational efficiency. This contributes to the high robustness of SwinUNETR for clinical deployment.

As cardiovascular disease (CVD) is the leading cause of noncancer mortality in colorectal or gastric cancer patients, it is essential to identify patients at increased CVD risk. Coronary artery calcium (CAC) is an established predictor of atherosclerotic CVD; however, its application is limited in this population. This study evaluates the association between automated CAC scoring using chest computed tomography and atherosclerotic CVD risk in colorectal or gastric cancer patients. A retrospective cohort study was conducted using electronic health records linked to claims data of colorectal or gastric cancer patients who underwent non-ECG-gated chest computed tomography at 2 tertiary hospitals in South Korea between 2011 and 2019. CAC was automatically quantified using deep learning software and used to classify patients into 4 groups (CAC=0, 0<CAC≤100, 100<CAC≤400, CAC>400). The primary outcome was major adverse cardiovascular events (myocardial infarction, stroke, or cardiovascular mortality), and assessed using the multivariable Fine and Gray subdistribution hazard model. A meta-analysis was performed to calculate pooled subdistribution hazard ratios. A total of 3153 patients were included in this study (36.5% female; 36.3% CAC=0; 38.1% 0<CAC≤100; 14.1% 100<CAC≤400; 11.5% CAC>400). The mean follow-up period was 4.1 years. The incidence rate of MACE was 5.28, 8.03, 9.99, and 29.14 per 1000 person-years in CAC=0, 0<CAC≤100, 100<CAC≤400, and CAC>400. Compared with CAC=0, the risk of MACE was not significantly different in patients with 0<CAC≤100 (subdistribution hazard ratio, 1.43 [95% CI, 0.41-5.01]), and 100<CAC≤400 (subdistribution hazard ratio, 0.99 [95% CI, 0.48-2.04]). Patients with CAC>400 had 2.33 (95% CI, 1.24-4.39) times higher risk of MACE compared with those with CAC=0. CAC>400 was associated with an increased risk of MACE compared with CAC=0 among colorectal or gastric cancer patients. CAC quantified on routine chest computed tomography scans provides prognostic information for atherosclerotic CVD risk in this population.

The Varian Ethos system allows not only on-treatment-table plan adaptation but also automated contouring with the aid of artificial intelligence. This study evaluates the initial clinical implementation of an adaptive pelvic radiotherapy system, focusing on the treatment times and the associated learning curve. We analyzed the data from 903 consecutive treatments for most urogenital cancers at our center. The treatment time was calculated from the time of the first cone-beam computed tomography scan used for replanning until the end of treatment. To calculate whether treatments were generally shorter over time, we divided the date of the first treatment into 3-months quartiles. Differences between the groups were calculated using t-tests. The mean time from the first cone-beam computed tomography scan to the end of treatment was 25.9min (standard deviation: 6.9min). Treatment time depended on the number of planning target volumes and treatment of the pelvic lymph nodes. The mean time from cone-beam computed tomography to the end of treatment was 37 % longer if the pelvic lymph nodes were treated and 26 % longer if there were more than two planning target volumes. There was a learning curve: in linear regression analysis, both quartiles of months of treatment (odds ratio [OR]: 1.3, 95 % confidence interval [CI]: 1.8-0.70, P<0.001) and the number of planning target volumes (OR: 3.0, 95 % CI: 2.6-3.4, P<0.001) were predictive of treatment time. Approximately two-thirds of the treatments were delivered within 33min. Treatment time was strongly dependent on the number of separate planning target volumes. There was a continuous learning curve.

Prostate-specific membrane antigen (PSMA) is an important target for positron emission tomography (PET) with computed tomography (CT) in prostate cancer. In addition to overexpression in prostate cancer cells, PSMA is expressed in healthy cells in the proximal tubules of the kidneys. Consequently, PSMA PET is being explored for renal functional imaging. Left and right renal uptake of PSMA targeted radiopharmaceuticals have shown strong correlations to split renal function (SRF) as determined by other methods. Manual segmentation of kidneys in PET images is, however, time consuming, making this method of measuring SRF impractical. In this study, we designed, trained and validated an artificial intelligence (AI) model for automatic renal segmentation and measurement of SRF in [¹⁸F]PSMA-1007 PET images. Kidneys were segmented in 135 [¹⁸F]PSMA-1007 PET/CT studies used to train the AI model. The model was evaluated in 40 test studies. Left renal function percentage (LRF%) measurements ranged from 40 to 67%. Spearman correlation coefficients for LRF% measurements ranged between 0.98 and 0.99 when comparing segmentations made by 3 human readers and the AI model. The largest LRF% difference between any measurements in a single case was 3 percentage points. The AI model produced measurements similar to those of human readers. Automatic measurement of SRF in PSMA PET is feasible. A potential use could be to provide additional data in investigation of renal functional impairment in patients treated for prostate cancer.