Cervical cancer remains a significant global health issue, with accurate differentiation between low-grade (LSIL) and high-grade squamous intraepithelial lesions (HSIL) crucial for effective screening and management. Current methods, such as Pap smears and HPV testing, often fall short in sensitivity and specificity. Deep learning models hold the potential to enhance the accuracy of cervical cancer screening but require thorough evaluation to ascertain their practical utility. This study compares the performance of two advanced deep learning models, SEResNet101 and SE-VGG19, in classifying cervical lesions using a dataset of 3,305 high-quality colposcopy images. We assessed the models based on their accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The SEResNet101 model demonstrated superior performance over SE-VGG19 across all evaluated metrics. Specifically, SEResNet101 achieved a sensitivity of 95%, a specificity of 97%, and an AUC of 0.98, compared to 89% sensitivity, 93% specificity, and an AUC of 0.94 for SE-VGG19. These findings suggest that SEResNet101 could significantly reduce both over- and under-treatment rates by enhancing diagnostic precision. Our results indicate that SEResNet101 offers a promising enhancement over existing screening methods, integrating advanced deep learning algorithms to significantly improve the precision of cervical lesion classification. This study advocates for the inclusion of SEResNet101 in clinical workflows to enhance cervical cancer screening protocols, thereby improving patient outcomes. Future work should focus on multicentric trials to validate these findings and facilitate widespread clinical adoption.

This work aimed to develop an automated method for quantifying the distribution and severity of perfusion changes on CT pulmonary angiography (CTPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) and to assess their associations with clinical parameters and expert annotations. Following automated segmentation of the chest, a machine-learning model assuming three distributions of attenuation in the pulmonary parenchyma (hyperemic, normal, and oligemic) was fitted to the attenuation histogram of CTPA images using Bayesian analysis. The proportion of each component, its spatial heterogeneity (entropy), and center-to-periphery distribution of the attenuation were calculated and correlated with the findings on CTPA semi-quantitatively evaluated by radiologists and with clinical function tests. CTPA scans from 52 patients (mean age, 65.2 ± 13.0 years; 27 men) diagnosed with CTEPH were analyzed. An inverse correlation was observed between the proportion of normal parenchyma and brain natriuretic propeptide (proBNP, ρ = -0.485, p = 0.001), mean pulmonary arterial pressure (ρ = -0.417, p = 0.002) and pulmonary vascular resistance (ρ = -0.556, p < 0.0001), mosaic attenuation (ρ = -0.527, p < 0.0001), perfusion centralization (ρ = -0.489, p = < 0.0001), and right ventricular diameter (ρ = -0.451, p = 0.001). The entropy of hyperemic parenchyma showed a positive correlation with the pulmonary wedge pressure (ρ = 0.402, p = 0.003). The slope of center-to-periphery attenuation distribution correlated with centralization (ρ = -0.477, p < 0.0001), and with proBNP (ρ = -0.463, p = 0.002). This study validates an automated system that leverages Bayesian analysis to quantify the severity and distribution of perfusion changes in CTPA. The results show the potential of this method to support clinical evaluations of CTEPH by providing reproducible and objective measures. Question This study introduces an automated method for quantifying the extent and spatial distribution of pulmonary perfusion abnormalities in CTEPH using variational Bayesian estimation. Findings Quantitative measures describing the extent, heterogeneity, and distribution of perfusion changes demonstrate strong correlations with key clinical hemodynamic indicators. Clinical relevance The automated quantification of perfusion changes aligns closely with radiologists' evaluations, delivering a standardized, reproducible measure with clinical relevance.

Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly increases mortality risk. While magnetic resonance imaging (MRI) offers a non-invasive assessment, accurately segmenting cirrhotic livers presents substantial challenges due to morphological alterations and heterogeneous signal characteristics. Deep learning approaches show promise for automating these tasks, but progress has been limited by the absence of large-scale, annotated datasets. Here, we present CirrMRI600+, the first comprehensive dataset comprising 628 high-resolution abdominal MRI scans (310 T1-weighted and 318 T2-weighted sequences, totaling nearly 40,000 annotated slices) with expert-validated segmentation labels for cirrhotic livers. The dataset includes demographic information, clinical parameters, and histopathological validation where available. Additionally, we provide benchmark results from 11 state-of-the-art deep learning experiments to establish performance standards. CirrMRI600+ enables the development and validation of advanced computational methods for cirrhotic liver analysis, potentially accelerating progress toward automated Cirrhosis visual staging and personalized treatment planning.

C2 pars interarticularis length (C2PIL) required for pars screws has not been thoroughly studied in subjects with high-riding vertebral artery (HRVA). We aimed to measure C2PIL specifically on the sides with HRVA, define short pars, optimal pars screw length, and incorporate C2PIL into HRVA clusters using machine learning algorithms. A clinical anatomical study based on cervical CT was conducted with STROBE-compliant case-control design. HRVA was defined as accepted. Interobserver, intraobserver, and inter-software agreement coefficients for HRVA were adopted from our previous study. Sample size was estimated with pwr package and C2PIL was measured. Cut-off value and predictive statistics of C2PIL for HRVA were computed with cutpointr package. Unsupervised machine learning clustering was applied with all three pars parameters. 345 potential screw insertion sites (PSIS) were grouped as HRVA (143 PSIS in 110 subjects) or controls (202 PSIS in 101 subjects). 68% participants were females. The median C2PIL in HRVA group was 13.7 mm with interquartile range (IQR) of 1.7, whereas in controls it was 19.8 mm (IQR = 2.7). The optimal cut-off value of C2PIL discriminating HRVA was 16.06 mm with sensitivity of 96.5% and specificity of 99.3%. Therefore, clinically important short pars was defined as ≤ 16 mm rounding to the nearest screw length. Two clusters were created incorportating three parameters of pars interarticularis. In preoperative planning, the identified C2PIL cut-off of ≤ 16 mm may assist surgeons in early recognition of HRVA. The average screw lengths of 14 mm for bicortical and 12 mm for safer unicortical purchase in HRVA cases may serve as practical intraoperative reference points, particularly in situations requiring rapid decision-making or when navigation systems are unavailable. Moreover, C2PIL complements the classic HRVA parameters within the dichotomized clustering framework.

This study aimed to compare two deep learning reconstruction (DLR) techniques (AiCE mild; AiCE strong) with two established methods-iterative reconstruction (IR) and filtered back projection (FBP)-for the detection of monosodium urate (MSU) in dual-energy computed tomography (DECT). An ex vivo bio-phantom and a raster phantom were prepared by inserting syringes containing different MSU concentrations and scanned in a 320-rows volume DECT scanner at different tube currents. The scans were reconstructed in a soft tissue kernel using the four reconstruction techniques mentioned above, followed by quantitative assessment of MSU volumes and image quality parameters, i.e., signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Both DLR techniques outperformed conventional IR and FBP in terms of volume detection and image quality. Notably, unlike IR and FBP, the two DLR methods showed no positive correlation of the MSU detection rate with the CT dose index (CTDIvol) in the bio-phantom. Our study highlights the potential of DLR for DECT imaging in gout, where it offers enhanced detection sensitivity, improved image contrast, reduced image noise, and lower radiation exposure. Further research is needed to assess the clinical reliability of this approach.

The aim of this study was to retrain our existing deep learning-based fully automated aortic valve leaflets/root measurement algorithm, using computed tomography (CT) data for root dilatation (RD), and assess its clinical feasibility. 67 ECG-gated cardiac CT scans were retrospectively collected from 40 patients with RD to retrain the algorithm. An additional 100 patients' CT data with aortic stenosis (AS, n=50) and aortic regurgitation (AR) with/without RD (n=50) were collected to evaluate the algorithm. 45 AR patients had RD. The algorithm provided patient-specific 3-dimensional aortic valve/root visualization. The measurements of 100 cases automatically obtained by the algorithm were compared with an expert's manual measurements. Overall, there was a moderate-to-high correlation, with differences of 6.1-13.4 mm²for the virtual basal ring area, 1.1-2.6 mm for sinus diameter, 0.1-0.6 mm for coronary artery height, 0.2-0.5 mm for geometric height, and 0.9 mm for effective height, except for the sinotubular junction of the AR cases (10.3 mm) with an indefinite borderline over the dilated sinuses, compared with 2.1 mm in AS cases. The measurement time (122 s) per case by the algorithm was significantly shorter than those of the experts (618-1,126 s). This fully automated algorithm can assist in evaluating aortic valve/root anatomy for planning surgical and transcatheter treatments while saving time and minimizing workload.

Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has significant potential to advance the capabilities of nuclear neuroimaging. The current and emerging applications of ML and DL in the processing, analysis, enhancement and interpretation of SPECT and PET imaging are explored for brain imaging. Key developments include automated image segmentation, disease classification, and radiomic feature extraction, including lower dimensionality first and second order radiomics, higher dimensionality third order radiomics and more abstract fourth order deep radiomics. DL-based reconstruction, attenuation correction using pseudo-CT generation, and denoising of low-count studies have a role in enhancing image quality. AI has a role in sustainability through applications in radioligand design and preclinical imaging while federated learning addresses data security challenges to improve research and development in nuclear cerebral imaging. There is also potential for generative AI to transform the nuclear cerebral imaging space through solutions to data limitations, image enhancement, patient-centered care, workflow efficiencies and trainee education. Innovations in ML and DL are re-engineering the nuclear neuroimaging ecosystem and reimagining tomorrow's precision medicine landscape.

Permanent prostate brachytherapy has inherent intraoperative organ deformation due to the inflatable trans-rectal ultrasound probe cover. Since the majority of the dose is delivered postoperatively with no deformation, the dosimetry approved at the time of implant may not accurately represent the dose delivered to the target and organs at risk. We aimed to evaluate the biological effect of the prostate deformation and its correlation with patient-reported outcomes. We prospectively acquired ultrasound images of the prostate pre- and postprobe cover inflation for 27 patients undergoing I-125 seed implant. The coordinates of implanted seeds from approved clinical plan were transferred to deformation-corrected prostate to simulate the actual dosimetry using a machine learning-based deformable image registration. The DVHs of both sets of plans were reduced to biologically effective dose (BED) distribution and subsequently to Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) metrics. The change in fourteen patient-reported rectal and urinary symptoms between pretreatment to 6 months post-op time points were correlated with the TCP and NTCP metrics using the area under the curve (AUC) and odds ratio (OR). Between the clinical and the deformation corrected research plans, the mean TCP decreased by 9.4% (p < 0.01), whereas mean NTCP of rectum decreased by 10.3% and that of urethra increased by 16.3%, respectively (p < 0.01). For the diarrhea symptom, the deformation corrected research plans showed AUC=0.75 and OR = 8.9 (1.3-58.8) for the threshold NTCP>20%, while the clinical plan showed AUC=0.56 and OR = 1.4 (0.2 to 9.0). For the symptom of urinary control, the deformation corrected research plans showed AUC = 0.70, OR = 6.9 (0.6 to 78.0) for the threshold of NTCP>15%, while the clinical plan showed AUC = 0.51 and no positive OR. Taking organ deformation into consideration, clinical brachytherapy plans showed worse tumor coverage, worse urethra sparing but better rectal sparing. The deformation corrected research plans showed a stronger correlation with the patient-reported outcome than the clinical plans for the symptoms of diarrhea and urinary control.

The diagnostic accuracy and subjectivity of existing Knee Osteoarthritis (OA) ordinal grading systems has been a subject of on-going debate and concern. Existing automated solutions are trained to emulate these imperfect systems, whilst also being reliant on large annotated databases for fully-supervised training. This work proposes a three stage approach for automated continuous grading of knee OA that is built upon the principles of Anomaly Detection (AD); learning a robust representation of healthy knee X-rays and grading disease severity based on its distance to the centre of normality. In the first stage, SS-FewSOME is proposed, a self-supervised AD technique that learns the 'normal' representation, requiring only examples of healthy subjects and <3% of the labels that existing methods require. In the second stage, this model is used to pseudo label a subset of unlabelled data as 'normal' or 'anomalous', followed by denoising of pseudo labels with CLIP. The final stage involves retraining on labelled and pseudo labelled data using the proposed Dual Centre Representation Learning (DCRL) which learns the centres of two representation spaces; normal and anomalous. Disease severity is then graded based on the distance to the learned centres. The proposed methodology outperforms existing techniques by margins of up to 24% in terms of OA detection and the disease severity scores correlate with the Kellgren-Lawrence grading system at the same level as human expert performance. Code available at https://github.com/niamhbelton/SS-FewSOME_Disease_Severity_Knee_Osteoarthritis.