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The present study addresses the segmentation and the 3D reconstruction of the corpus callosum from MRI scans. Accurate segmentation of the corpus callosum is essential in order to enable its reconstruction and 3D visualization to facilitate early diagnosis. In fact, many studies have established a strong correlation between the shape of the corpus callosum and several pathological conditions. However, the segmentation is made difficult by regions of similar intensity within the MRI images. To overcome this challenge, we propose an automated method that relies mainly on a probabilistic neural network applied to superpixels. The proposed scheme involves segmenting the corpus callosum within the MRI scans, followed by the application of the marching cubes technique in order to generate 3D volumes. The effectiveness of the proposed method has been extensively validated on four challenging datasets (OASIS, ABIDE, MIRIAD, and SBD), and the obtained results demonstrate its superior performance compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Key Clinical Message: Gliosarcoma, a rare cerebral neoplasm, has not been linked to hippocampal changes in cats. We report a case of complex partial seizures with orofacial involvement, revealing gliosarcoma concurrent with bilateral hippocampal sclerosis. A 16‐year‐old neutered female domestic shorthair cat presented with acute inappetence, ataxia, disorientation, and vacant staring. Brain MRI revealed an ill‐defined, round, intra‐axial mass in the right piriform lobe, showing hyperintensity on T2W, T2‐FLAIR, and T2*W, and hypointensity on T1W images. The lesion exhibited mass effect and contrast enhancement in its center. Bilateral hyperintensity on T2‐FLAIR images and contrast enhancement were observed in the hippocampus. Brain histologic and immunohistochemical analysis revealed cerebral gliosarcoma with concurrent hippocampal sclerosis. Feline LGI1‐antibody testing on the serum and/or CSF was not performed due to insufficient biomaterial. Although retrospective testing on brain tissue was considered, it ultimately proved unfeasible, preventing us from ruling out antibody‐associated limbic encephalitis. In conclusion, cerebral gliosarcoma should be included in feline intracranial tumor differentials, warranting brain MRI and feline LGI1‐antibody testing in cats showing complex partial seizures with orofacial involvement. In our case, the prognosis remained poor due to the presence of a high‐grade glioma. [ABSTRACT FROM AUTHOR]

The human brain comprises a complex interconnection of nerve cells and vital organs, which regulates crucial bodily processes. Although neurons commonly undergo developmental stages, they may occasionally experience abnormalities, leading to abnormal growths known as brain tumors. The objective of brain tumor segmentation is to produce precise boundaries of brain tumor regions. This study extensively analyzes deep learning methods for brain tumor detection, evaluating their effectiveness across diverse datasets. It introduces a hybrid model, which is proposed by the name HybriCSF: hybrid convolutional-SVM-fuzzy C-means model combining convolutional neural network (CNN) with the classifier support vector machine (SVM) and clustering technique fuzzy C-means (FCM). The proposed model was implemented on Br35H, BraTs 2020 and BraTs2021 datasets. The suggested model outperformed the existing methods by achieving 98.6% of accuracy on Br35H dataset and dice score of 0.63, 0.87, 0.81 on BraTs 2020 dataset for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively. The achieved dice scores on the BraTs 2021datasets are 0.89, 0.95, and 0.89 for ET, WT, and TC, respectively. The results show that the suggested model HybriCSF outperforms the other CNN-based models in terms of accuracy. [ABSTRACT FROM AUTHOR]

Purpose of review: This article aims to illustrate the current state of investigations and management of liver metastases in patients with Neuroendocrine Neoplasms. Neuroendocrine tumours (NETs) are rising in incidence globally and have become the second most prevalent gastrointestinal malignancy in UK and USA. Frequently, patients have metastatic disease at time of presentation. The liver is the most common site of metastases for gastro-enteropancreatic NETs. Characterisation of liver metastases with imaging is important to ensure disease is not under-staged. Recent Findings: Magnetic resonance imaging and positron emission tomography are now becoming standard of care for imaging liver metastases. There is an increasing armamentarium of therapies available for management of NETs and loco-regional therapy for liver metastases. The data supporting surgical and loco-regional therapy is reviewed with focus on role of liver transplantation. Summary: It is important to use appropriate imaging and classification of NET liver metastases. It is key that decisions regarding approach to treatment is undertaken in a multidisciplinary team and that individualised approaches are considered for management of patients with metastatic NETs. [ABSTRACT FROM AUTHOR]

Abstract Background The study aimed to determine the grade of retraction and atrophy according to the time elapsed in traumatic isolated full-thickness supraspinatus (SS) tears in young patients. Methods One thousand twenty-six patients, who underwent arthroscopic shoulder surgery, were retrospectively reviewed. Pre-operative magnetic resonance imaging (MRI) of 69 patients aged 18 to 40 years with isolated traumatic full-thickness SS lesions remaining after exclusion criteria were evaluated for tendon retraction and atrophy grades. SS retraction was determined from a T2-weighted oblique coronal MRI slice, and the atrophy grade was determined from the T1-weighted oblique sagittal MRI slice. The patients were divided into four groups 0–1 month, 1–3 months, 3–6 months, and 6–12 months according to the time between trauma and MRI. The relationship of tendon retraction and muscle atrophy with elapsed time was evaluated, in addition, comparisons between groups were made. Results Thirty-one (45%) of the patients were female and their mean age was 30 ± 7.3 (18–40) years. The mean age of men was 30.5 ± 6.9 (18–39) years (p = 0.880). The time between rupture and MRI was moderately correlated with retraction and strongly correlated with atrophy grades (r = 0.599, 0.751, respectively). It was observed that there was a statistically significant difference between the 1st (0–1 month) and 2nd (1–3 months) groups (p = 0.003, 0.001, respectively), and between the 2nd and 3rd (3–6 months) groups (p = 0.032, 0.002, respectively), but there was no significant difference between the 3rd and 4th (6–12 months) groups (p = 0.118, 0.057, respectively). In addition, there was a moderate correlation between tendon retraction and atrophy grades (r = 0.668). Power (1- b) in post hoc analysis was calculated as 0.826. Conclusions The current study, supported by arthroscopy, showed that there is a moderate and strong positive correlation between the time elapsed after trauma and the level of retraction and degree of atrophy in traumatic full-thickness SS tears, and demonstrated the importance of early surgical intervention in young patients.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with early diagnosis being crucial for effective intervention. Magnetic Resonance Imaging (MRI) is a valuable tool for detecting structural brain changes associated with AD. However, accurate and automated analysis of MRI scans remains a challenge due to the complexity and variability in brain structures. Traditional methods for analyzing MRI scans for AD diagnosis often rely on manual interpretation or basic image processing techniques, which can be time-consuming and prone to variability. There is a need for advanced automated methods that can accurately segment brain structures and extract relevant features for reliable diagnosis. This study proposes a novel approach for AD diagnosis using MRI scans, combining Conditional Attention U-Net for segmentation and Ant Colony Optimization (ACO) for feature extraction. The Conditional Attention U-Net enhances segmentation accuracy by incorporating conditional attention mechanisms to focus on relevant features while minimizing background noise. ACO is employed to optimize feature extraction by simulating the foraging behavior of ants, which efficiently selects and refines key features related to AD. The proposed model was evaluated on a dataset of 500 MRI scans, comparing performance with traditional methods using metrics such as Dice Similarity Coefficient (DSC) and classification accuracy. The Conditional Attention U-Net achieved an average DSC of 0.89 for segmentation of key brain regions, outperforming conventional methods by 10%. The ACO-enhanced feature extraction resulted in a classification accuracy of 92% for AD diagnosis, representing a 7% improvement over baseline methods. The combination of these techniques demonstrated a significant enhancement in both segmentation precision and diagnostic accuracy, showcasing the effectiveness of the proposed approach for early AD detection. [ABSTRACT FROM AUTHOR]

Accurate detection and segmentation of brain cancer in MRI scans are critical for effective diagnosis and treatment planning. Traditional methods often struggle with the complexities of tumor morphology and variations in scan quality. Existing detection systems can be slow and may not effectively handle the variability in tumor appearances, leading to potential delays in diagnosis and treatment. To address these challenges, we propose an enhanced detection framework using a Siamese Regional Proposed Network (SRPN). The SRPN integrates template branch and bounding box regression to expedite detection processes. The system utilizes an extended Siamese network to learn the distance between tracklet pairs, capturing the local and global features of tumors. These features are transferred to bidirectional gated recurrent units (GRUs), which generate tracklets and segment them into shorter sub-tracklets based on local distances. The segmented subtracklets are then reconnected into longer trajectories using similarities derived from temporal pooling global features. Additionally, fuzzy logic fusion is employed to combine segmented regions for improved accuracy. The SRPN-based framework demonstrated a significant improvement in detection speed and accuracy. Experimental results show an accuracy increase of 12% over traditional methods, achieving 94% accuracy with a detection time reduction of 30%. The system also improved segmentation precision, with a mean Intersection over Union (IoU) score of 85%, compared to 75% in conventional approaches. [ABSTRACT FROM AUTHOR]

Brain tumors occur due to the expansion of abnormal cell tissues and can be malignant (cancerous) or benign (not cancerous). Numerous factors such as the position, size, and progression rate are considered while detecting and diagnosing brain tumors. Detecting brain tumors in their initial phases is vital for diagnosis where MRI (magnetic resonance imaging) scans play an important role. Over the years, deep learning models have been extensively used for medical image processing. The current study primarily investigates the novel Fine-Tuned Vision Transformer models (FTVTs)--FTVT-b16, FTVT-b32, FTVT-l16, FTVT-l32--for brain tumor classification, while also comparing them with other established deep learning models such as ResNet50, MobileNet-V2, and EfficientNet - B0. A dataset with 7,023 images (MRI scans) categorized into four different classes, namely, glioma, meningioma, pituitary, and no tumor are used for classification. Further, the study presents a comparative analysis of these models including their accuracies and other evaluation metrics including recall, precision, and F1-score across each class. The deep learning models ResNet-50, EfficientNet-B0, and MobileNet-V2 obtained an accuracy of 96.5%, 95.1%, and 94.9%, respectively. Among all the FTVT models, FTVT-l16 model achieved a remarkable accuracy of 98.70% whereas other FTVT models FTVT-b16, FTVTb32, and FTVT-132 achieved an accuracy of 98.09%, 96.87%, 98.62%, respectively, hence proving the efficacy and robustness of FTVT's in medical image processing. [ABSTRACT FROM AUTHOR]

Background: The study aimed to determine the grade of retraction and atrophy according to the time elapsed in traumatic isolated full-thickness supraspinatus (SS) tears in young patients. Methods: One thousand twenty-six patients, who underwent arthroscopic shoulder surgery, were retrospectively reviewed. Pre-operative magnetic resonance imaging (MRI) of 69 patients aged 18 to 40 years with isolated traumatic full-thickness SS lesions remaining after exclusion criteria were evaluated for tendon retraction and atrophy grades. SS retraction was determined from a T2-weighted oblique coronal MRI slice, and the atrophy grade was determined from the T1-weighted oblique sagittal MRI slice. The patients were divided into four groups 0–1 month, 1–3 months, 3–6 months, and 6–12 months according to the time between trauma and MRI. The relationship of tendon retraction and muscle atrophy with elapsed time was evaluated, in addition, comparisons between groups were made. Results: Thirty-one (45%) of the patients were female and their mean age was 30 ± 7.3 (18–40) years. The mean age of men was 30.5 ± 6.9 (18–39) years (p = 0.880). The time between rupture and MRI was moderately correlated with retraction and strongly correlated with atrophy grades (r = 0.599, 0.751, respectively). It was observed that there was a statistically significant difference between the 1st (0–1 month) and 2nd (1–3 months) groups (p = 0.003, 0.001, respectively), and between the 2nd and 3rd (3–6 months) groups (p = 0.032, 0.002, respectively), but there was no significant difference between the 3rd and 4th (6–12 months) groups (p = 0.118, 0.057, respectively). In addition, there was a moderate correlation between tendon retraction and atrophy grades (r = 0.668). Power (1- b) in post hoc analysis was calculated as 0.826. Conclusions: The current study, supported by arthroscopy, showed that there is a moderate and strong positive correlation between the time elapsed after trauma and the level of retraction and degree of atrophy in traumatic full-thickness SS tears, and demonstrated the importance of early surgical intervention in young patients. [ABSTRACT FROM AUTHOR]

Currently, the prevalence of musculoskeletal injuries in high-level competitive athletes is high, and the injuries mainly appear in the neck and lower back. Long training years, looking down during training, and engaging in real-time combat sports are the main risk factors for the occurrence of neck disorders, and therefore, a scientific method is needed to identify signs of musculoskeletal injuries in athletes' MRI scans for early prevention and early intervention. Therefore, in this paper, a network model based on the lightweight network MobilenetV2 is proposed based on the transfer learning method, which ensures high classification accuracy while completing the recognition task with less number of parameters and computation. To address the problem that deep learning models require a large amount of data for training and patient images are difficult to obtain, this paper proposes the RS algorithm, which masks the local features of the 2DMRI images, augments the dataset, and combines it with the two proposed deep migratory learning classification models, TLV2 and TLV2C, in which TLV2 is the MobileNetV2 that fixes the shallow layer and initializes the deep layer after training on the Image Net dataset. network, initialize the deep network and re-train it under MRI dataset. TLV2C redesigns the classifier on the basis of TLV2. The experimental results show that the algorithm proposed in this paper solves the problem of insufficient MRI data, and at the same time is able to identify the signs of musculoskeletal injuries in MRI scans of athletes. [ABSTRACT FROM AUTHOR]

Alzheimer’s Disease (AD) is characterized by progressive cognitive decline, and the prevalence of the disease continues to rise as the population ages. With over 10 million new cases yearly, there is still no established way to quickly and accurately classify the stage a patient with Alzheimer’s has progressed to. Inaccurate classification leads to delayed or incorrect treatment, financial implications, and emotional distress, ultimately severely impacting both the patient and their family. This paper proposes a novel deep learning Convolutional Neural Network (CNN) model to assist in the classification of a patient's AD. The model development began with a dataset of over 6,000 pre-labeled Magnetic Resonance Imaging (MRI) scans categorized into four stages: normal, very-mild, mild, and moderate AD. Using transfer learning with InceptionV3, the framework analyzes class proportions and fine-tunes a CNN to leverage pre-trained features. The CNN then undergoes rigorous training with early stopping based on validation Area Under Curve (AUC). Upon completing the training, a comprehensive model evaluation is conducted, encompassing metrics such as AUC and confusion matrices. Using these methods, three different mini-models are constructed, all with different training parameters and number of epochs. Conclusively, the successful models are saved, and an array of evaluation metrics such as accuracy, precision, F1 score, and recall are then used to analyze the models' results. The results show that the proposed method achieves 95.0% or more across all metrics in seconds, demonstrating a promising classification performance that can greatly assist doctors in the classification of AD, helping achieve not only higher diagnostic accuracy but also allowing for faster diagnosis. The results of this model demonstrate significant potential to enhance patient care and highlight the impact of Artificial Intelligence (AI) on AD diagnosis. With the ability to classify MRIs with 20 percent greater accuracy and reduce diagnostic time from months to seconds, the promise of AI in not only AD diagnosis, but the whole of healthcare will enable us to take steps toward a better world. Envisioning a future where the healthcare system can prioritize patient wants rather than just needs. Abbreviations: AD - Alzheimer’s Disease; AI - Artificial Intelligence; AUC – Area Under Curve; CNN - Convolutional Neural Network; RNN - Recurrent Neural Networks; ROC - Receiver Operating Characteristic

Key Clinical Message Gliosarcoma, a rare cerebral neoplasm, has not been linked to hippocampal changes in cats. We report a case of complex partial seizures with orofacial involvement, revealing gliosarcoma concurrent with bilateral hippocampal sclerosis. Abstract A 16‐year‐old neutered female domestic shorthair cat presented with acute inappetence, ataxia, disorientation, and vacant staring. Brain MRI revealed an ill‐defined, round, intra‐axial mass in the right piriform lobe, showing hyperintensity on T2W, T2‐FLAIR, and T2*W, and hypointensity on T1W images. The lesion exhibited mass effect and contrast enhancement in its center. Bilateral hyperintensity on T2‐FLAIR images and contrast enhancement were observed in the hippocampus. Brain histologic and immunohistochemical analysis revealed cerebral gliosarcoma with concurrent hippocampal sclerosis. Feline LGI1‐antibody testing on the serum and/or CSF was not performed due to insufficient biomaterial. Although retrospective testing on brain tissue was considered, it ultimately proved unfeasible, preventing us from ruling out antibody‐associated limbic encephalitis. In conclusion, cerebral gliosarcoma should be included in feline intracranial tumor differentials, warranting brain MRI and feline LGI1‐antibody testing in cats showing complex partial seizures with orofacial involvement. In our case, the prognosis remained poor due to the presence of a high‐grade glioma.

Purpose: To establish the clinical applicability of deep‐learning organ‐at‐risk autocontouring models (DL‐AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI‐based models. The correlation between geometric and dosimetric measures was also investigated to establish whether dosimetric assessment is required for clinical validation. Method: CT and MRI‐based deep learning autosegmentation models were trained using edited and unedited clinical contours. Autosegmentations were dosimetrically compared to gold standard contours for a test cohort. D1%, D5%, D50%, and maximum dose were used as clinically relevant dosimetric measures. The statistical significance of dosimetric differences between the gold standard and autocontours was established using paired Student's t‐tests. Clinically significant cases were identified via dosimetric headroom to the OAR tolerance. Pearson's Correlations were used to investigate the relationship between geometric measures and absolute percentage dose changes for each autosegmentation model. Results: Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the CT DL‐AC models or between the MRI DL‐AC for any investigated brain OARs. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than other OARs, for all autosegmentation models. A weak correlation was consistently observed between the outcomes of dosimetric and geometric evaluations. Conclusions: Editing contours before training the DL‐AC model had no significant impact on dosimetry. The geometric test metrics were inadequate to estimate the impact of contour inaccuracies on dose. Accordingly, dosimetric analysis is needed to evaluate the clinical applicability of DL‐AC models in the brain. [ABSTRACT FROM AUTHOR]

Background: This study evaluates the performance of a vision transformer (ViT) model, ViT-b16, in classifying ischemic stroke cases from Moroccan MRI scans and compares it to the Visual Geometry Group 16 (VGG-16) model used in a prior study. Methods: A dataset of 342 MRI scans, categorized into 'Normal' and 'Stroke' classes, underwent preprocessing using TensorFlow's tf.data API. Results: The ViT-b16 model was trained and evaluated, yielding an impressive accuracy of 97.59%, surpassing the VGG-16 model's 90% accuracy. Conclusions: This research highlights the ViT-b16 model's superior classification capabilities for ischemic stroke diagnosis, contributing to the field of medical image analysis. By showcasing the efficacy of advanced deep learning architectures, particularly in the context of Moroccan MRI scans, this study underscores the potential for real-world clinical applications. Ultimately, our findings emphasize the importance of further exploration into AI-based diagnostic tools for improving healthcare outcomes. [ABSTRACT FROM AUTHOR]

The integration of multiple data sources is becoming increasingly important in the field of medical imaging for the purpose of improving predictions. In this study, we investigated the use of a unique combination of imaging and genomic data to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT). Utilizing a stacking generalization approach, our model achieved promising results. The classification report from the stacking ensemble shows a precision of 0.82, recall of 0.81, and an F1-score of 0.80. These findings demonstrate the potential of integrating diverse data sources in medical imaging to enhance predictive capabilities, specifically in the context of MGMT methylation status. Our study highlights the importance of ongoing research to refine and optimize the integration of multiple data sources, ultimately leading to more accurate predictions and tailored treatment strategies. This research has the potential to significantly impact patient outcomes by facilitating personalized interventions based on precise predictions of MGMT methylation status. [ABSTRACT FROM AUTHOR]

Brain tumors occur due to the expansion of abnormal cell tissues and can be malignant (cancerous) or benign (not cancerous). Numerous factors such as the position, size, and progression rate are considered while detecting and diagnosing brain tumors. Detecting brain tumors in their initial phases is vital for diagnosis where MRI (magnetic resonance imaging) scans play an important role. Over the years, deep learning models have been extensively used for medical image processing. The current study primarily investigates the novel Fine-Tuned Vision Transformer models (FTVTs)—FTVT-b16, FTVT-b32, FTVT-l16, FTVT-l32—for brain tumor classification, while also comparing them with other established deep learning models such as ResNet50, MobileNet-V2, and EfficientNet - B0. A dataset with 7,023 images (MRI scans) categorized into four different classes, namely, glioma, meningioma, pituitary, and no tumor are used for classification. Further, the study presents a comparative analysis of these models including their accuracies and other evaluation metrics including recall, precision, and F1-score across each class. The deep learning models ResNet-50, EfficientNet-B0, and MobileNet-V2 obtained an accuracy of 96.5%, 95.1%, and 94.9%, respectively. Among all the FTVT models, FTVT-l16 model achieved a remarkable accuracy of 98.70% whereas other FTVT models FTVT-b16, FTVT-b32, and FTVT-132 achieved an accuracy of 98.09%, 96.87%, 98.62%, respectively, hence proving the efficacy and robustness of FTVT’s in medical image processing.

Aims: Osteochondral lesions of the talus (OLT) are a common cause of disability and chronic ankle pain. Many operative treatment strategies have been introduced; however, they have their own disadvantages. Recently lesion repair using autologous cartilage chip has emerged therefore we investigated the efficacy of particulated autologous cartilage transplantation (PACT) in OLT. Methods: We retrospectively analyzed 32 consecutive symptomatic patients with OLT who underwent PACT with minimum one-year follow-up. Standard preoperative radiography and MRI were performed for all patients. Follow-up second-look arthroscopy or MRI was performed with patient consent approximately one-year postoperatively. Magnetic resonance Observation of Cartilage Repair Tissue (MOCART) score and International Cartilage Repair Society (ICRS) grades were used to evaluate the quality of the regenerated cartilage. Clinical outcomes were assessed using the pain visual analogue scale (VAS), Foot Function Index (FFI), and Foot Ankle Outcome Scale (FAOS). Results: All patients had ICRS grade IV cartilage lesions, except for one (ICRS grade III). The paired MOCART scores significantly improved from 42.5 (SD 1.53) to 63.5 (SD 22.60) (p = 0.025) in ten patients. Seven patients agreed to undergo second-look arthroscopy; 5 patients had grade I (normal) ICRS scores and two patients had grade II (nearly normal) ICRS scores. VAS, FFI, and all subscales of FAOS were significantly improved postoperatively (p ≤ 0.003). Conclusion: PACT significantly improved the clinical, radiological, and morphological outcomes of OLT. We consider this to be a safe and effective surgical method based on the short-term clinical results of this study. Cite this article: Bone Jt Open 2023;4(12):942–947.

Peripheral neuropathy is known to be one of the most common neurological disorders. Despite the great diagnostic value of electroneuromyography and ultrasound, addressing the diagnostics and differential diagnostics of peripheral nerve diseases of different origin could be challenging. In recent years, magnetic resonance tomography has been increasingly used for evaluating cases of suspected or established peripheral neuropathy with excellent results. This manuscript mainly deals with the advantages and limitations of the aforementioned diagnostic instruments, technical considerations according to different anatomy of peripheral nerves, along with state-of-the-art technical decisions, frequently used magnetic resonance imaging sequences and their diagnostic value based on own observation, and recommendations for contrast enhancement use and different methods of fat suppression. Currently, there is practically no standardized description of normal magnetic resonance imaging features of peripheral nerves, as well as their changes in different diseases. The evaluation of images is mainly based on the radiologist experience, which obviously decreases method’s diagnostic value. Studies of large numbers involving healthy volunteers and patients with peripheral neuropathies of different origin are required to address this issue.

Abstract Background Human face is complex and intricate structure, with several features that are unique to each individual and can be taken as an identity pass in this mortal world. The facial profile of an individual holds materialistic value and is crucial for human identification. Present study aims to investigate the influence of sex and age of an individual on the soft tissue thicknesses (FSTT) at midline facial landmarks estimated on MRI scans. Methods Facial soft tissue thickness was estimated at 20 midline landmarks on the MRI scans of 120 (57 male; 63 female) anatomically normal Northwest Indian adult subjects (18–70 + years age-group). Age and sex dependent variations in tissue thicknesses at different landmarks were estimated using statistical software IBM SPSS Statistics 23. Results The results of the study yielded that most of the midline landmarks had higher values in males than in females indicating observable sexual dimorphism in FSTT values in this facial region, statistically significant differences were also noted in soft tissue thickness estimates of different age-groups, showing increasing or decreasing trend with age. The mid-facial landmarks like mid-philtrum, supra-dentale, incisor superius, and labile inferius displayed highly significant p values. Conclusions Present study results were in tandem with the findings of previous studies; males and younger age-group individuals were having higher tissue thickness values than the females and older individuals, respectively. The highest soft tissue estimates were obtained for the middle age subjects and the most aged individuals had the least midline soft tissue thickness values. These results can have significant forensic anthropological repercussions in attempting facial approximations on unknown skulls and identification of unknown individuals from still images, photographs or videos.

Aims: Classifying trochlear dysplasia (TD) is useful to determine the treatment options for patients suffering from patellofemoral instability (PFI). There is no consensus on which classification system is more reliable and reproducible for the purpose of guiding clinicians’ management of PFI. There are also concerns about the validity of the Dejour Classification (DJC), which is the most widely used classification for TD, having only a fair reliability score. The Oswestry-Bristol Classification (OBC) is a recently proposed system of classification of TD, and the authors report a fair-to-good interobserver agreement and good-to-excellent intraobserver agreement in the assessment of TD. The aim of this study was to compare the reliability and reproducibility of these two classifications. Methods: In all, six assessors (four consultants and two registrars) independently evaluated 100 axial MRIs of the patellofemoral joint (PFJ) for TD and classified them according to OBC and DJC. These assessments were again repeated by all raters after four weeks. The inter- and intraobserver reliability scores were calculated using Cohen’s kappa and Cronbach’s α. Results: Both classifications showed good to excellent interobserver reliability with high α scores. The OBC classification showed a substantial intraobserver agreement (mean kappa 0.628; p < 0.005) whereas the DJC showed a moderate agreement (mean kappa 0.572; p < 0.005). There was no significant difference in the kappa values when comparing the assessments by consultants with those by registrars, in either classification system. Conclusion: This large study from a non-founding institute shows both classification systems to be reliable for classifying TD based on axial MRIs of the PFJ, with the simple-to-use OBC having a higher intraobserver reliability score than that of the DJC. Cite this article: Bone Jt Open 2023;4(7):532–538.

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Otitis media (OM) is among the most common of childhood illnesses. It has long been hypothesized that children under age two are predisposed to OM due to differences in the anatomy of the Eustachian tube (ET), including the angle of the ET. OM in later childhood is less common but does occur, begging the question, are there shape differences in the ET that persist underlying later occurrences of OM? To answer this question, a novel method, which applied geometric and morphometric shape analysis to landmarks obtained from MRI data, was used. MRI scans were performed on 16 children (5 control, 3 cOME, and 8 rAOM) between 2011 and 2015. Sixteen landmarks representing the shape of the ET, cranial base, and palate were analyzed. The results of a Procrustes ANOVA indicate that the shape of the ET varies significantly (p < 0.01) between the OM and control groups. The shape differences between the OM group and the control are a medial and low attachment site of the tensor veli palatini (TVP) muscle, a posterior and high torus tubarius, and an anteriorly projected palate. These results support previous findings that a relatively horizontal ET is associated with a predisposition for OM. This study used a novel approach to examine anatomical differences in children with and without OM. First, the data set is unique in that it includes MRI scans of children with a confirmed OM diagnosis. Second, the use of MRI scans in craniofacial anatomy OM research is novel and allows for the collection of soft tissue landmarks and the visualization of soft tissue structures. Third, geometric morphometric shape analysis is a statistical method that captures shape differences, offering a more universal picture of nuanced changes within the entire set of landmarks, in contrast to more traditional linear and angular measurements used in prior OM studies examining craniofacial anatomy. [ABSTRACT FROM AUTHOR]

To develop an ABP-MRI to evaluate response to NAC for invasive breast carcinoma. A single-center, cross-sectional study. A consecutive series of 210 women with invasive breast carcinoma who underwent breast MRI after NAC between 2016 and 2020. 1.5 T / Dynamic contrast-enhanced. MRI scans were independently reevaluated, with access to dynamic contrast-enhanced without contrast and to the first, second, and third post-contrast time (ABP-MRI 1–3). The diagnostic performance of the ABP-MRIs and the Full protocol (FP-MRI) were analyzed. The Wilcoxon non-parametric test (p -value <0.050) was used to compare the capability in measuring the most extensive residual lesion. The median age was 47 (24–80) years. ABP-MRI 1 showed higher specificity (84.6%; 77/91) but a higher probability of false-negatives (16.8%) and lower sensitivity (83.2%; 99/119) than ABP-MRI 2,3 and the FP-MRI, which were identical in specificity (81.3%; 74/91), probability of false-negatives (8.4%), and sensitivity (91.6%; 109/119). ABP-MRI 2 showed a mean underestimation of only 0.03 cm in the measurement of the longest axis of the residual lesion (p = 0.008) with an average reduction in the acquisition time of 75%, compared with the FP-MRI. ABP-MRI 2 showed diagnostic performance equivalent to the FP-MRI with a 75% reduction in the acquisition time. [ABSTRACT FROM AUTHOR]

Background: Human face is complex and intricate structure, with several features that are unique to each individual and can be taken as an identity pass in this mortal world. The facial profile of an individual holds materialistic value and is crucial for human identification. Present study aims to investigate the influence of sex and age of an individual on the soft tissue thicknesses (FSTT) at midline facial landmarks estimated on MRI scans. Methods: Facial soft tissue thickness was estimated at 20 midline landmarks on the MRI scans of 120 (57 male; 63 female) anatomically normal Northwest Indian adult subjects (18–70 + years age-group). Age and sex dependent variations in tissue thicknesses at different landmarks were estimated using statistical software IBM SPSS Statistics 23. Results: The results of the study yielded that most of the midline landmarks had higher values in males than in females indicating observable sexual dimorphism in FSTT values in this facial region, statistically significant differences were also noted in soft tissue thickness estimates of different age-groups, showing increasing or decreasing trend with age. The mid-facial landmarks like mid-philtrum, supra-dentale, incisor superius, and labile inferius displayed highly significant p values. Conclusions: Present study results were in tandem with the findings of previous studies; males and younger age-group individuals were having higher tissue thickness values than the females and older individuals, respectively. The highest soft tissue estimates were obtained for the middle age subjects and the most aged individuals had the least midline soft tissue thickness values. These results can have significant forensic anthropological repercussions in attempting facial approximations on unknown skulls and identification of unknown individuals from still images, photographs or videos. [ABSTRACT FROM AUTHOR]

Purpose: Brain tumors are among the most lethal forms of cancer, so early diagnosis is crucial. As a result of machine learning algorithms, radiologists can now make accurate diagnoses of tumors without resorting to invasive procedures. There are, however, a number of obstacles to overcome. To begin, classifying brain tumors presents a significant difficulty in developing the most effective deep learning framework. Furthermore, physically dividing the brain tumor is a time-consuming and challenging process that requires the expertise of medical professionals. Methods: Here, we have discussed the use of a fuzzy 3D highlighting method for the segmentation of brain tumors and the selection of suspect tumor areas based on the geometric characteristics of MRI scans. After features were extracted from the brain tumor section, the images were classified using two machine learning methods: a support vector machine technique optimized with the grasshopper optimization algorithm (GOA-SVM), and a deep neural network technique based on features selected with the genetic algorithm (GA-DNN). This classifies brain tumors into benign and malignant. Implemented on the MATLAB platform, the proposed method is evaluated for effectiveness using performance metrics like sensitivity, accuracy, specificity, and Youden index. Results: From these results, it is clear that the proposed strategy is significantly superior to the alternatives. The average classification accuracy was determined to be 97.53%, 97.65%, for GA-DNN and GOA-SVM, respectively. Conclusion: These findings may be a quick and important step to detect the presence of lesions at the same time as cancerous tumors in neurology diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: People with primary malignant brain tumors (PMBT) undergo anti-tumor treatment and are followed up with MRI interval scans. There are potential burdens and benefits to interval scanning, yet high-quality evidence to suggest whether scans are beneficial or alter outcomes of importance for patients is lacking. We aimed to gain an in-depth understanding of how adults living with PMBTs experience and cope with interval scanning. Methods: Twelve patients diagnosed with WHO grade III or IV PMBT from two sites in the UK took part. Using a semi-structured interview guide, they were asked about their experiences of interval scans. A constructivist grounded theory approach was used to analyze data. Results: Although most participants found interval scans uncomfortable, they accepted that scans were something that they had to do and were using various coping methods to get through the MRI scan. All participants said that the wait between their scan and results was the most difficult part. Despite the difficulties they experienced, all participants said that they would rather have interval scans than wait for a change in their symptoms. Most of the time, scans provided relief, gave participants some certainty in an uncertain situation, and a short-term sense of control over their lives. Conclusion: The present study shows that interval scanning is important and highly valued by patients living with PMBT. Although interval scans are anxiety provoking, they appear to help people living with PMBT cope with the uncertainty of their condition. [ABSTRACT FROM AUTHOR]

Gastrointestinal (GI) cancer is the most common cancer in men and women. GI cancers are increasing every year worldwide. In the biomedical industry, Radiation treatment is a frequent choice for treating cancers of the GI tract in which the oncologist focuses the high range of X-ray beams on the tumor while avoiding the healthy organs. Manual segmentation of healthy organs to focus X-ray beams only on the tumor portion is very tedious and time-consuming, which can lead the treatment from a few minutes to hours. Deep learning techniques can concur with this problem by segmentation of healthy organs. This research article proposes a deep learning-based model Pyramid Scene Parsing Network (PSPNet) for segmenting organs such as the stomach, small bowel, and large bowel in the GI tract. The model has been simulated with five feature encoding networks: ResNext 50, Timm_Gernet_S, ResNet 34, EfficientNet B1, and MobileNet V2. These encoders were used for downsampling the feature map in the PSPNet architecture. The implementations have been performed using the UW Madison GI tract dataset, which contains 38,496 MRI scans of cancer patients. The model was evaluated using validation dice, Jaccard, and validation loss. The results reveal that the PSPNet model combined with ResNet 34 as encoder outperforms the other feature encoding networks with validation dice as 0.8842, validation Jaccard as 0.8531, and validation loss as 0.1365. Radiation oncologists can use the proposed model to speed up radiation therapy for cancer treatment.

AimsThe evidence demonstrating the superiority of early MRI has led to increased use of MRI in clinical pathways for acute wrist trauma. The aim of this study was to describe the radiological characteristics and the inter-observer reliability of a new MRI based classification system for scaphoid injuries in a consecutive series of patients.MethodsWe identified 80 consecutive patients with acute scaphoid injuries at one centre who had presented within four weeks of injury. The radiographs and MRI scans were assessed by four observers, two radiologists, and two hand surgeons, using both pre-existing classifications and a new MRI based classification tool, the Oxford Scaphoid MRI Assessment Rating Tool (OxSMART). The OxSMART was used to categorize scaphoid injuries into three grades: contusion (grade 1); unicortical fracture (grade 2); and complete bicortical fracture (grade 3).ResultsIn total there were 13 grade 1 injuries, 11 grade 2 injuries, and 56 grade 3 injuries in the 80 consecutive patients. The inter-observer reliability of the OxSMART was substantial (Kappa = 0.711). The inter-observer reliability of detecting an obvious fracture was moderate for radiographs (Kappa = 0.436) and MRI (Kappa = 0.543). Only 52% (29 of 56) of the grade 3 injuries were detected on plain radiographs. There were two complications of delayed union, both of which occurred in patients with grade 3 injuries, who were promptly treated with cast immobilization. There were no complications in the patients with grade 1 and 2 injuries and the majority of these patients were treated with early mobilization as pain allowed.ConclusionThis MRI based classification tool, the OxSMART, is reliable and clinically useful in managing patients with acute scaphoid injuries.Cite this article: Bone Jt Open 2022;3(11):913–920.

Abstract Background The aim of this study is to evaluate both the utility of MRI scans and reports used in the current practice routine of shoulder surgeons and their surgical decision-making process. Methods Ninety-three shoulder-specialised orthopaedic surgeons of the Canadian Shoulder and Elbow Society (CSES) Orthopaedic Association were surveyed in 2020 anonymously online to help identify the use of MR-imaging and reports in managing shoulder disorders and surgical decision process. Results Thirty out of 93 (32.25%) CSES fellowship-trained orthopaedic surgeons participated. Respondents request MRI scans in about 55% of rotator cuff (RC) pathology and 48% of shoulder instability cases. Fifty percent of patients with potential RC pathology arrive with a completed MRI scan prior first orthopaedic consult. Their surgical decision is primarily based on patient history (45–55%) and physical examination (23–42%) followed by MRI scan review (2.6–18%), reading MRI reports (0–1.6%) or viewing other imaging (3–23%) depending on the shoulder disease. Ninety percent of surgeons would not decide on surgery in ambiguous cases unless the MR-images were personally reviewed. Respondents stated that shoulder MRI scans are ordered too frequently prior specialist visit as identified in more than 50% of cases depending on pathology. Conclusions The decision-making process for shoulder surgery depends on the underlying pathology and patient history. The results demonstrate that orthopaedic surgeons are comfortable reviewing shoulder MRI scans without necessarily reading the MRI report prior to a surgical decision. MRI scans are becoming an increasingly important part of surgical management in shoulder pathologies but should not be used without assessment of patient history and or physical examination.

Background: A magnetic resonance imaging (MRI) procedure can cause preprocedural and periprocedural anxiety in children. Psychosocial interventions are used to prepare children for the procedure to alleviate anxiety, but these interventions are time-consuming and costly, limiting their clinical use. Virtual reality (VR) is a promising way to overcome these limitations in the preparation of children before an MRI scan. Objective: The objective of this study is (1) to develop a VR smartphone intervention to prepare children at home for an MRI procedure; and (2) to examine the effect of the VR intervention in a randomized controlled trial, in which the VR intervention will be compared to care as usual (CAU). CAU involves an information letter about an MRI examination. The primary outcome is the child's procedural anxiety during the MRI procedure. Secondary outcomes include preprocedural anxiety and parental anxiety. We hypothesize that the VR preparation will result in a higher reduction of the periprocedural anxiety of both parents and children as compared to CAU. Methods: The VR intervention provides a highly realistic and child-friendly representation of an MRI environment. In this randomized controlled trial, 128 children (aged 6 to 14 years) undergoing an MRI scan will be randomly allocated to the VR intervention or CAU. Children in the VR intervention will receive a log-in code for the VR app and are sent cardboard VR glasses. Results: The VR smartphone preparation app was developed in 2020. The recruitment of participants is expected to be completed in December 2022. Data will be analyzed, and scientific papers will be submitted for publication in 2023. Conclusions: The VR smartphone app is expected to significantly reduce pre- and periprocedural anxiety in pediatric patients undergoing an MRI scan. The VR app offers a realistic and child-friendly experience that can contribute to modern care. A smartphone version of the VR app has the advantage that children, and potentially their parents, can get habituated to the VR environment and noises in their own home environment and can do this VR MRI preparation as often and as long as needed. [ABSTRACT FROM AUTHOR]

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Otitis media (OM) is among the most common of childhood illnesses. It has long been hypothesized that children under age two are predisposed to OM due to differences in the anatomy of the Eustachian tube (ET), including the angle of the ET. OM in later childhood is less common but does occur, begging the question, are there shape differences in the ET that persist underlying later occurrences of OM? To answer this question, a novel method, which applied geometric and morphometric shape analysis to landmarks obtained from MRI data, was used. MRI scans were performed on 16 children (5 control, 3 cOME, and 8 rAOM) between 2011 and 2015. Sixteen landmarks representing the shape of the ET, cranial base, and palate were analyzed. The results of a Procrustes ANOVA indicate that the shape of the ET varies significantly (p < 0.01) between the OM and control groups. The shape differences between the OM group and the control are a medial and low attachment site of the tensor veli palatini (TVP) muscle, a posterior and high torus tubarius, and an anteriorly projected palate. These results support previous findings that a relatively horizontal ET is associated with a predisposition for OM. This study used a novel approach to examine anatomical differences in children with and without OM. First, the data set is unique in that it includes MRI scans of children with a confirmed OM diagnosis. Second, the use of MRI scans in craniofacial anatomy OM research is novel and allows for the collection of soft tissue landmarks and the visualization of soft tissue structures. Third, geometric morphometric shape analysis is a statistical method that captures shape differences, offering a more universal picture of nuanced changes within the entire set of landmarks, in contrast to more traditional linear and angular measurements used in prior OM studies examining craniofacial anatomy.

Purpose: We have built a novel AI-driven QA method called AutoConfidence (ACo), to estimate segmentation confidence on a per-voxel basis without gold standard segmentations, enabling robust, efficient review of automated segmentation (AS). We have demonstrated this method in brain OAR AS on MRI, using internal and external (third-party) AS models., Methods: Thirty-two retrospectives, MRI planned, glioma cases were randomly selected from a local clinical cohort for ACo training. A generator was trained adversarialy to produce internal autosegmentations (IAS) with a discriminator to estimate voxel-wise IAS uncertainty, given the input MRI. Confidence maps for each proposed segmentation were produced for operator use in AS editing and were compared with "difference to gold-standard" error maps. Nine cases were used for testing ACo performance on IAS and validation with two external deep learning segmentation model predictions [external model with low-quality AS (EM-LQ) and external model with high-quality AS (EM-HQ)]. Matthew's correlation coefficient (MCC), false-positive rate (FPR), false-negative rate (FNR), and visual assessment were used for evaluation. Edge removal and geometric distance corrections were applied to achieve more useful and clinically relevant confidence maps and performance metrics., Results: ACo showed generally excellent performance on both internal and external segmentations, across all OARs (except lenses). MCC was higher on IAS and low-quality external segmentations (EM-LQ) than high-quality ones (EM-HQ). On IAS and EM-LQ, average MCC (excluding lenses) varied from 0.6 to 0.9, while average FPR and FNR were ≤0.13 and ≤0.21, respectively. For EM-HQ, average MCC varied from 0.4 to 0.8, while average FPR and FNR were ≤0.37 and ≤0.22, respectively., Conclusion: ACo was a reliable predictor of uncertainty and errors on AS generated both internally and externally, demonstrating its potential as an independent, reference-free QA tool, which could help operators deliver robust, efficient autosegmentation in the radiotherapy clinic., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

Cite this article: Bone Joint Res 2023;12(5):309–310.

Alzheimer's disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85–3.88%, 2.43–2.66%, and 1.8–40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively. [ABSTRACT FROM AUTHOR]