In recent years, the application of artificial intelligence (AI) in medical image analysis has drawn increasing attention in clinical studies of gynecologic tumors. This study presents the development and prospects of AI applications to assist in the treatment of gynecological oncology. The Web of Science database was screened for articles published until August 2023. "artificial intelligence," "deep learning," "machine learning," "radiomics," "radiotherapy," "chemoradiotherapy," "neoadjuvant therapy," "immunotherapy," "gynecological malignancy," "cervical carcinoma," "cervical cancer," "ovarian cancer," "endometrial cancer," "vulvar cancer," "Vaginal cancer" were used as keywords. Research articles related to AI-assisted treatment of gynecological cancers were included. A total of 317 articles were retrieved based on the search strategy, and 133 were selected by applying the inclusion and exclusion criteria, including 114 on cervical cancer, 10 on endometrial cancer, and 9 on ovarian cancer. Among the included studies, 44 (33%) focused on prognosis prediction, 24 (18%) on treatment response prediction, 13 (10%) on adverse event prediction, five (4%) on dose distribution prediction, and 47 (35%) on target volume delineation. Target volume delineation and dose prediction were performed using deep Learning methods. For the prediction of treatment response, prognosis, and adverse events, 57 studies (70%) used conventional radiomics methods, 13 (16%) used deep Learning methods, 8 (10%) used spatial-related unconventional radiomics methods, and 3 (4%) used temporal-related unconventional radiomics methods. In cervical and endometrial cancers, target prediction mostly included treatment response, overall survival, recurrence, toxicity undergoing radiotherapy, lymph node metastasis, and dose distribution. For ovarian cancer, the target prediction included platinum sensitivity and postoperative complications. The majority of the studies were single-center, retrospective, and small-scale; 101 studies (76%) had single-center data, 125 studies (94%) were retrospective, and 127 studies (95%) included Less than 500 cases. The application of AI in assisting treatment in gynecological oncology remains limited. Although the results of AI in predicting the response, prognosis, adverse events, and dose distribution in gynecological oncology are superior, it is evident that there is no validation of substantial data from multiple centers for these tasks.

The clinical significance of medullary abnormalities in the appendicular skeleton detected by computed tomography (CT) in patients with multiple myeloma (MM) remains incompletely elucidated. This study aims to validate novel low-dose CT-based methods for quantifying myeloma bone marrow (BM) volume in the appendicular skeleton of MM patients undergoing [¹⁸F]FDG PET/CT. Seventy-two newly diagnosed, transplantation eligible MM patients enrolled in the randomised phase 3 GMMG-HD7 trial underwent whole-body [¹⁸F]FDG PET/CT prior to treatment and after induction therapy with either isatuximab plus lenalidomide, bortezomib, and dexamethasone or lenalidomide, bortezomib, and dexamethasone alone. Two CT-based methods using the Medical Imaging Toolkit (MITK 2.4.0.0, Heidelberg, Germany) were used to quantify BM infiltration in the appendicular skeleton: (1) Manual approach, based on calculation of the highest mean CT value (CTv) within bony canals. (2) Semi-automated approach, based on summation of CT values across the appendicular skeleton to compute cumulative CT values (cCTv). PET/CT data were analyzed visually and via standardized uptake value (SUV) metrics, applying the Italian Myeloma criteria for PET Use (IMPeTUs). Additionally, an AI-based method was used to automatically derive whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) from PET scans. Post-induction, all patients were evaluated for minimal residual disease (MRD) using BM multiparametric flow cytometry. Correlation analyses were performed between imaging data and clinical, histopathological, and cytogenetic parameters, as well as treatment response. Statistical significance was defined as p < 0.05. At baseline, the median CTv (manual) was 26.1 Hounsfield units (HU) and the median cCTv (semi-automated) was 5.5 HU. Both CT-based methods showed weak but significant correlations with disease burden indicators: CTv correlated with BM plasma cell infiltration (r = 0.29; p = 0.02) and β2-microglobulin levels (r = 0.28; p = 0.02), while cCTv correlated with BM plasma cell infiltration (r = 0.25; p = 0.04). Appendicular CT values further demonstrated significant associations with PET-derived parameters. Notably, SUVmax values from the BM of long bones were strongly correlated with both CTv (r = 0.61; p < 0.001) and moderately with cCTv (r = 0.45; p < 0.001). Patients classified as having increased [¹⁸F]FDG uptake in the BM (Deauville Score ≥ 4), according to the IMPeTUs criteria, exhibited significantly higher CTv and cCTv values compared to those with Deauville Score <4 (p = 0.002 for both). AI-based analysis of PET data revealed additional weak-to-moderate significant associations, with MTV correlating with CTv (r = 0.32; p = 0.008) and cCTv (r = 0.45; p < 0.001), and TLG showing correlations with CTv (r = 0.36; p = 0.002) and cCTv (r = 0.46; p < 0.001). Following induction therapy, CT values decreased significantly from baseline (median CTv = -13.8 HU, median cCTv = 5.2 HU; p < 0.001 for both), and CTv significantly correlated with SUVmax values from the BM of long bones (r = 0.59; p < 0.001). In parallel, the incidence of follow-up pathological PET/CT scans, SUV values, Deauville Scores, and AI-derived MTV and TLG values showed a significant reduction after therapy (all p < 0.001). No significant differences in CTv, cCTv, or PET-derived metrics were observed between MRD-positive and MRD-negative patients. Novel CT-based quantification approaches for assessing BM involvement in the appendicular skeleton correlate with key clinical and PET parameters in MM. As low-dose, standardized techniques, they show promise for inclusion in MM imaging protocols, potentially enhancing assessment of disease extent and treatment response.

Advances in fetal three-dimensional (3D) ultrasound (US) and magnetic resonance imaging (MRI) have revolutionized the study of fetal brain development, enabling detailed analysis of brain structures and growth. Despite their complementary capabilities, these modalities capture fundamentally different physical signals, potentially leading to systematic differences in image-derived phenotypes (IDPs). Here, we evaluate the agreement of IDPs between US and MRI by comparing the volumes of eight brain structures from 90 subjects derived using deep-learning algorithms from majority same-day imaging (days between scans: mean = 1.2, mode = 0 and max = 4). Excellent agreement (intra-class correlation coefficient, <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mi>ICC</mi> <mo>></mo> <mn>0.75</mn></mrow> <annotation>$$ ICC>0.75 $$</annotation></semantics> </math> ) was observed for the cerebellum, cavum septum pellucidum, thalamus, white matter and deep grey matter volumes, with significant correlations <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <mfenced><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </mfenced> </mrow> <annotation>$$ \left(p<0.001\right) $$</annotation></semantics> </math> for most structures, except the ventricular system. Bland-Altman analysis revealed some systematic biases: intracranial and cortical plate volumes were larger on US than MRI, by an average of <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mn>35</mn> <mspace></mspace> <msup><mi>cm</mi> <mn>3</mn></msup> </mrow> <annotation>$$ 35\ {\mathrm{cm}}^3 $$</annotation></semantics> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mn>4.1</mn> <mspace></mspace> <msup><mi>cm</mi> <mn>3</mn></msup> </mrow> <annotation>$$ 4.1\ {\mathrm{cm}}^3 $$</annotation></semantics> </math> , respectively. Finally, we found the labels of the brainstem and ventricular system were not comparable between the modalities. These findings highlight the necessity of structure-specific adjustments when interpreting fetal brain IPDs across modalities and underscore the complementary roles of US and MRI in advancing fetal neuroimaging.

Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by progressive loss of dopaminergic neurons in substantia nigra, resulting in both motor impairments and cognitive decline. Traditional PD classification methods are expert-dependent and time-intensive, while existing deep learning (DL) models often suffer from inconsistent accuracy, limited interpretability, and inability to fully capture PD's clinical heterogeneity. This study proposes a novel framework Enhanced EfficientNet-Extended Multimodal PD Classification with Hybrid Particle Swarm and Grey Wolf Optimizer (EEFN-XM-PDC-HybPS-GWO) to overcome these challenges. The model integrates T1-weighted MRI, DaTscan images, and gait scores from NTUA and PhysioNet repository respectively. Denoising is achieved via Multiscale Attention Variational Autoencoders (MSA-VAE), and critical regions are segmented using Semantic Invariant Multi-View Clustering (SIMVC). The Enhanced EfficientNet-Extended Multimodal (EEFN-XM) model extracts and fuses image and gait features, while HybPS-GWO optimizes classification weights. The system classifies subjects into early-stage PD, advanced-stage PD, and healthy controls (HCs). Ablation analysis confirms the hybrid optimizer's contribution to performance gains. The proposed model achieved 99.2% accuracy with stratified 5-fold cross-validation, outperforming DMFEN-PDC, MMT-CA-PDC, and LSTM-PDD-GS by 7.3%, 15.97%, and 10.43%, respectively, and reduced execution time by 33.33%. EEFN-XM-PDC-HybPS-GWO demonstrates superior accuracy, computational efficiency, and clinical relevance, particularly in early-stage diagnosis and PD classification.

Medical Image plays a vital role in diagnosis, but noise in patient scans severely affects the accuracy and quality of images. Denoising methods are important to increase the clarity of these images, particularly in low-resource settings where current diagnostic roles are inaccessible. Pneumonia is a widespread disease that presents significant diagnostic challenges due to the high similarity between its various types and the lack of medical images for emerging variants. This study introduces a novel Diffusion with swin transformer-based Optimized Crow Search algorithm to increase the image's quality and reliability. This technique utilizes four datasets such as brain tumor MRI dataset, chest X-ray image, chest CT-scan image, and BUSI. The preprocessing steps involve conversion to grayscale, resizing, and normalization to improve image quality in medical image (MI) datasets. Gaussian noise is introduced to further enhance image quality. The method incorporates a diffusion process, swin transformer networks, and optimized crow search algorithm to improve the denoising of medical images. The diffusion process reduces noise by iteratively refining images while swin transformer captures complex image features that help differentiate between noise and essential diagnostic information. The crow search optimization algorithm fine-tunes the hyperparameters, which minimizes the fitness function for optimal denoising performance. The method is tested across four datasets, indicating its optimal effectiveness against other techniques. The proposed method achieves a peak signal-to-noise ratio of 38.47 dB, a structural similarity index measure of 98.14%, a mean squared error of 0.55, and a feature similarity index measure of 0.980, which outperforms existing techniques. These outcomes reflect that the proposed approach effectively enhances the quality of images, resulting in precise and dependable diagnoses.

The brain tumour (BT) is an aggressive disease among others, which leads to a very short life expectancy. Therefore, early and prompt treatment is the main stage in enhancing patients' quality of life. Biomedical imaging permits the non-invasive evaluation of diseases, depending upon visual assessments that lead to better medical outcome expectations and therapeutic planning. Numerous image techniques like computed tomography (CT), magnetic resonance imaging (MRI), etc., are employed for evaluating cancer in the brain. The detection, segmentation and extraction of diseased tumour regions from biomedical images are a primary concern, but are tiresome and time-consuming tasks done by clinical specialists, and their outcome depends on their experience only. Therefore, the use of computer-aided technologies is essential to overcoming these limitations. Recently, artificial intelligence (AI) models have been very effective in enhancing performance and improving the method of medical image diagnosis. This paper proposes an Enhanced Brain Tumour Segmentation through Biomedical Imaging and Feature Model Fusion with Bonobo Optimiser (EBTS-BIFMFBO) model. The main intention of the EBTS-BIFMFBO model relies on enhancing the segmentation and classification model of BTs utilizing advanced models. Initially, the EBTS-BIFMFBO technique follows bilateral filter (BF)-based noise elimination and CLAHE-based contrast enhancement. Furthermore, the proposed EBTS-BIFMFBO model involves a segmentation process by the DeepLabV3 + model to identify tumour regions for accurate diagnosis. Moreover, the fusion models such as InceptionResNetV2, MobileNet, and DenseNet201 are employed for the feature extraction. Additionally, the convolutional sparse autoencoder (CSAE) method is implemented for the classification process of BT. Finally, the hyper-parameter selection of CSAE is performed by the bonobo optimizer (BO) method. A vast experiment is conducted to highlight the performance of the EBTS-BIFMFBO approach under the Figshare BT dataset. The comparison results of the EBTS-BIFMFBO approach portrayed a superior accuracy value of 99.16% over existing models.

Quantification of amyloid plaques (A), neurofibrillary tangles (T₂), and neurodegeneration (N) using PET and MRI is critical for Alzheimer's disease (AD) diagnosis and prognosis. Existing pipelines face limitations regarding processing time, tracer variability handling, and multimodal integration. We developed petBrain, a novel end-to-end processing pipeline for amyloid-PET, tau-PET, and structural MRI. It leverages deep learning-based segmentation, standardized biomarker quantification (Centiloid, CenTauR, HAVAs), and simultaneous estimation of A, T₂, and N biomarkers. It is implemented in a web-based format, requiring no local computational infrastructure and software usage knowledge. petBrain provides reliable, rapid quantification with results comparable to existing pipelines for A and T₂, showing strong concordance with data processed in ADNI databases. The staging and quantification of A/T₂/N by petBrain demonstrated good agreements with CSF/plasma biomarkers, clinical status and cognitive performance. petBrain represents a powerful open platform for standardized AD biomarker analysis, facilitating clinical research applications.

Many Parkinson's disease (PD) patients manifest complications related to treatment called levodopa-induced dyskinesia (LID). Preventing the onset of LID is crucial to the management of PD, but the reasons why some patients develop LID are unclear. The ability to prognosticate predisposition to LID would be valuable for the investigation of mitigation strategies. Thirty rats received 6-hydroxydopamine to induce Parkinsonism-like behaviors before treatment with levodopa (2 mg/kg) daily for 22 days. Fourteen developed LID-like behaviors. Fluorodeoxyglucose PET, T₂-weighted MRI and cerebral perfusion imaging were collected before treatment. Support vector machines were trained to classify prospective LID vs. non-LID animals from treatment-naïve baseline imaging. Volumetric perfusion imaging performed best overall with 86.16% area-under-curve, 86.67% accuracy, 92.86% sensitivity, and 81.25% specificity for classifying animals with LID vs. non-LID in leave-one-out cross-validation. We have demonstrated proof-of-concept for imaging-based classification of susceptibility to LID of a Parkinsonian rat model using perfusion-based imaging and a machine learning model.

Bottom-of-sulcus dysplasia (BOSD) is a diagnostically challenging subtype of focal cortical dysplasia, 60% being missed on magnetic resonance imaging (MRI). Automated MRI-based detection methods have been developed for focal cortical dysplasia, but not BOSD specifically, and few methods incorporate fluorodeoxyglucose positron emission tomography (FDG-PET) alongside MRI features. We report the development and performance of an automated BOSD detector using combined MRI + PET. The training set comprised 54 patients with focal epilepsy and BOSD. The test sets comprised 17 subsequently diagnosed patients with BOSD from the same center, and 12 published patients from a different center. Across training and test sets, 81% of patients had normal initial MRIs and most BOSDs were <1.5 cm³. In the training set, 12 features from T1-MRI, fluid-attenuated inversion recovery-MRI, and FDG-PET were evaluated to determine which features best distinguished dysplastic from normal-appearing cortex. Using the Multi-centre Epilepsy Lesion Detection group's machine-learning detection method with the addition of FDG-PET, neural network classifiers were then trained and tested on MRI + PET, MRI-only, and PET-only features. The proportion of patients whose BOSD was overlapped by the top output cluster, and the top five output clusters, were determined. Cortical and subcortical hypometabolism on FDG-PET was superior in discriminating dysplastic from normal-appearing cortex compared to MRI features. When the BOSD detector was trained on MRI + PET features, 87% BOSDs were overlapped by one of the top five clusters (69% top cluster) in the training set, 94% in the prospective test set (88% top cluster), and 75% in the published test set (58% top cluster). Cluster overlap was generally lower when the detector was trained and tested on PET-only or MRI-only features. Detection of BOSD is possible using established MRI-based automated detection methods, supplemented with FDG-PET features and trained on a BOSD-specific cohort. In clinically appropriate patients with seemingly negative MRI, the detector could suggest MRI regions to scrutinize for possible BOSD.

Prostate cancer (PCa) is one of the most common malignancies threatening men's health globally. A comprehensive and integrated approach is essential for its early screening, diagnosis, risk stratification, treatment guidance, and efficacy assessment. Radiomics, leveraging multi-parametric magnetic resonance imaging (mpMRI) and positron emission tomography/computed tomography (PET/CT), has demonstrated significant clinical value in the non-invasive diagnosis, aggressiveness assessment, and prognosis prediction of PCa, with substantial potential when combined with artificial intelligence. In genomics, mutations or deletions in genes such as TMPRSS2-ERG, PTEN, RB1, TP53, and DNA damage repair genes (e.g., BRCA1/2) are closely associated with disease development and progression, holding profound implications for diagnosis, treatment, and prognosis. Concurrently, biomarkers like prostate-specific antigen (PSA), novel urinary markers (e.g., PCA3), and circulating tumor cells (CTCs) are widely utilized in PCa research and management. Integrating these technologies into personalized treatment plans and the broader framework of precision medicine allows for an in-depth exploration of the relationship between specific biomarkers and disease pathogenesis. This review summarizes the current research on radiomics, genomics, and biomarkers in PCa, and discusses their future potential and applications in advancing individualized patient care.