Medical Imaging Research Articles

Photoinduced Charge Centralization Quenches the Fluorescence of Conjugation-Fused Tetrazine Labels with Red-to-Near-Infrared Emissions.

Tetrazine-derived fluorogenic labels are extensively studied for their potential in biological and medical imaging. Nonetheless, the fluorescence quenching mechanism in numerous precursors continues to be debated, particularly as the wavelengths extend into the red and near-infrared (NIR) regions. This challenge poses obstacles to systematically optimizing their fluorogenicity, i.e., achieving red-shifted wavelengths and improved fluorescence turn-on signals through click reactions. This paper highlights the significance of photoinduced charge centralization (PCC), a quenching mechanism observed in tetrazine-fused fluorogenic labels with integrated π-conjugations. PCC is primarily responsible for the quenching effects observed in such labels emitting in the red-to-NIR spectrum. Drawing from structure-property relationships, this study proposes two molecular design strategies that incorporate the PCC mechanism and constitutional isomerization to develop high-performance tetrazine-based labels. These strategies facilitate multiplex fluorescence imaging following click reactions, promising significant advancements in bio-orthogonal imaging techniques.

Machine Learning Approach for Medical Image Analysis

Machine Learning Approach for Medical Image Analysis

Statistical Analysis of Gastric Cancer Cells Response to Broadband Terahertz Radiation with and without Contrast Nanoparticles.

The paper describes the statistical analysis of the response of gastric cancer cells and normal cells to broadband terahertz radiation up to 4 THz, both with and without the use of nanostructured contrast agents. The THz spectroscopy analysis was comparatively performed under the ATR procedure and transmission measurement procedure. The statistical analysis was conducted towards multiple pairwise comparisons, including a support medium (without cells) versus a support medium with nanoparticles, normal cells versus normal cells with nanoparticles, and, respectively, tumor cells versus tumor cells with nanoparticles. When generally comparing the ATR procedure and transmission measurement procedure for a broader frequency domain, the differentiation between normal and tumor cells in the presence of contrast agents is superior when using the ATR procedure. THz contrast enhancement by using contrast agents derived from MRI-related contrast agents leads to only limited benefits and only for narrow THz frequency ranges, a disadvantage for THz medical imaging.

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis.

Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality. This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data. In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips. Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance. Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.

Transformers for colorectal cancer segmentation in CT imaging.

Most recently transformer models became the state of the art in various medical image segmentation tasks and challenges, outperforming most of the conventional deep learning approaches. Picking up on that trend, this study aims at applying various transformer models to the highly challenging task of colorectal cancer (CRC) segmentation in CT imaging and assessing how they hold up to the current state-of-the-art convolutional neural network (CNN), the nnUnet. Furthermore, we wanted to investigate the impact of the network size on the resulting accuracies, since transformer models tend to be significantly larger than conventional network architectures. For this purpose, six different transformer models, with specific architectural advancements and network sizes were implemented alongside the aforementioned nnUnet and were applied to the CRC segmentation task of the medical segmentation decathlon. The best results were achieved with the Swin-UNETR, D-Former, and VT-Unet, each transformer models, with a Dice similarity coefficient (DSC) of 0.60, 0.59 and 0.59, respectively. Therefore, the current state-of-the-art CNN, the nnUnet could be outperformed by transformer architectures regarding this task. Furthermore, a comparison with the inter-observer variability (IOV) of approx. 0.64 DSC indicates almost expert-level accuracy. The comparatively low IOV emphasizes the complexity and challenge of CRC segmentation, as well as indicating limitations regarding the achievable segmentation accuracy. As a result of this study, transformer models underline their current upward trend in producing state-of-the-art results also for the challenging task of CRC segmentation. However, with ever smaller advances in total accuracies, as demonstrated in this study by the on par performances of multiple network variants, other advantages like efficiency, low computation demands, or ease of adaption to new tasks become more and more relevant.

LC-MANet: Location-constrained joint optic disc and cup segmentation via multiplex aggregation network

Many early ophthalmology-related diseases can be detected through fundus images. The cup-to-disc ratio (CDR) is an important criterion for the early screening and diagnosis of glaucoma. However, accurately calculating it is a challenging task primarily due to blurred boundaries and the physiological structure of the fundus. To alleviate these issues, this paper designs a location-constrained joint optic disc (OD) and optic cup (OC) segmentation method using the multiplex aggregation network (LC-MANet). This method incorporates independent segmentation and joint segmentation within a coarse-to-fine framework. In the independent segmentation stage, this paper utilizes the initial OD and OC as prior location auto-context information to constrain the joint segmentation of OD and OC. The proposed multiplex aggregation network combines features from the corresponding layer and across layers to capture global and local information. This paper employs a multi-channel fusion strategy in image preprocessing to suppress vascular interference in OD independent segmentation. To emphasize the superiority of our method, it is important to highlight the significant number of test images used in the evaluation. Our experiments are conducted on three public datasets, namely RIM-ONE (60 images), Drishti-GS (51 images), and REFUGE (400 images). Experimental results demonstrate that our method outperforms most others in terms of Dice Coefficient and absolute error. The accuracy of glaucoma screening achieved by our proposed method for the Drishti-GS, REFUGE, and RIM-ONE-r3 datasets is 0.9216, 0.9600, and 0.9000, respectively. The proposed framework successfully designs a multiplex aggregation network for a coarse-to-fine joint segmentation model and achieves state-of-the-art performance in OD and OC segmentation. Furthermore, it can be applied to other medical image segmentation tasks.

Advancements in brain tumor identification: Integrating synthetic GANs with federated-CNNs in medical imaging analysis

Brain tumors are a significant health concern worldwide, necessitating accurate and timely diagnosis for effective treatment planning and management. However, conventional methods for brain tumor identification through medical imaging analysis often face challenges related to accuracy, efficiency, and privacy concerns. Current approaches may struggle with limited datasets, privacy regulations hindering data sharing, and the need for specialized expertise in interpreting medical images. Accurately identifying brain tumors is pivotal in diagnosis, treatment planning, and patient prognosis. This research proposes a novel approach for advancing brain tumor identification by integrating Synthetic Generative Adversarial Networks with federated convolutional neural Networks in medical imaging analysis. Federated-CNNs are a type of neural network architecture designed for federated learning scenarios. In federated learning, model training occurs locally on data distributed across multiple devices or institutions without exchanging raw data. Federated CNNs allow collaborative model training across these distributed datasets by aggregating local model updates rather than exchanging raw data. This approach ensures that sensitive data remain localized within each participating institution, thus addressing privacy concerns in medical imaging analysis. Our methodology harnesses the power of GANs to generate synthetic brain MRI images, addressing data scarcity issues commonly encountered in medical imaging datasets. These synthetic images are then utilized in conjunction with Federated-CNNs, enabling cooperative model training between many healthcare institutions while maintaining the anonymity and privacy of data. Moreover, integrating Federated CNNs ensures that sensitive medical imaging data remain localized within participating institutions, addressing data privacy concerns and fostering collaboration among medical professionals. The research advances medical imaging analysis by introducing a novel methodology that leverages existing technologies to improve brain tumor identification accuracy. Specifically, the feature extraction phase using DenseNet121, implemented in MATLAB, achieves an outstanding accuracy of 99.82 % and outperforms Various existing methods, including Inception-V3, ResNet-18, and GoogleNet, demonstrating the efficacy of our approach in capturing discriminative features from medical imaging data. This high accuracy underscores the potential of our methodology to enhance diagnostic accuracy and clinical decision-making in neurology and oncology. The research offers a promising avenue for further exploration and innovation in medical imaging analysis, with significant implications for improving patient outcomes and advancing healthcare practices.

Application of machine learning methods and medical image processing in solving the problem of detecting stenoses of the middle cerebral artery according to computed tomographic angiography data

Application of machine learning methods and medical image processing in solving the problem of detecting stenoses of the middle cerebral artery according to computed tomographic angiography data

In Situ Growth of Lead‐Free Double Perovskite Micron Sheets in Polymethyl Methacrylate for X‐Ray Imaging

AbstractPb‐free double‐perovskite (DP) scintillators are highly promising candidates for X‐ray imaging because of their superior optoelectronic properties, low toxicity, and high stability. However, practical applications require Pb‐free DP crystals to be ground and mixed with polymers to produce scintillator films. Grinding can compromise film uniformity and optical properties, thereby affecting imaging resolution. In this study, an in situ fabrication strategy is proposed to facilitate the crystalline growth of Pb‐free Cs2AgInxBi1‐xCl6 micron sheets in polymethyl methacrylate in a single step. By adjusting the In3+/Bi3+ ratio, Cs2AgIn0.9Bi0.1Cl6/PMMA composite films (CFs) with excellent scintillation properties are obtained, including a light yield of up to 32000 photons per MeV and an X‐ray detection limit of 87 nGyairs−1. This strategy also enabled the production of large Cs2AgIn0.9Bi0.1Cl6/PMMA CFs, which demonstrated favorable flexibility and stability, fabricating products with advanced eligibility for commercial applications. The CFs exhibited outstanding performances in X‐ray imaging, producing high‐resolution structures and providing a new avenue for the development of Pb‐free DP materials in fields such as medical imaging and safety detection.

Detection of Breast Cancer in Digital Mammograms by Using Artificial Intelligence

Abstract Background Daily reading of a large number of normal mammograms ni breast cancer screening programs is a tedious task reducing the attractiveness of the field. The advent of Artificial Intelligence (AI) in medical imaging could, however, provide means to improve the efficiency of mammography screening. Objective Our study aims to emphasize the role of AI system as an aid in detection of breast cancer in digital mammograms. Patients and Methods The main source of data for this study were the mammography studies on the picture archiving and communications system (PACS) of theradiology department, i n Shams university hospitals of the patients referred to the radiology department at Ain Shams university hospitals for screening mammography. Results Our study showed that the improvement in diagnosticperformance and reading times with AI support was observed for the radiologists who were least experienced with mammography while the Al is increased the reading times and had no effect on diagnostic performance on the more experienced radiologists. Conclusion Using the AI as an aid has proven to improve the overall performance of less experienced radiologists allowing them to reach expert like levels of diagnostic performance in the detection of breast cancer at mammography by using an AI computer system for support without the need for additional reading time.

P-182 High-resolution synthetic embryo images generated by AI

Abstract Study question How effectively can a latent diffusion model generate high-fidelity embryo images tailored to the specific contextual needs of researchers, based on the user’s text input? Summary answer The latent diffusion model successfully generated high-resolution embryo images, providing a novel approach to address data scarcity in embryo imaging. What is known already The use of AI in generating synthetic data has been increasingly explored to address the scarcity of real-world datasets in various fields. Particularly in medical imaging, AI-generated synthetic data offers a potential solution to overcome the limitations imposed by data privacy concerns and ethical considerations. Previous studies have shown that AI can replicate complex patterns in data, but its application in generating embryo images remains less explored. Study design, size, duration Single static images of 5,133 Day 5 blastocysts and 2,093 Day 3 cleavages were retrospectively collected from seven in vitro fertilization clinics between June 2011 and May 2022. The images were analyzed along with relevant metadata including clinical information and embryo grades. Day 3 embryo grading was based on the number of blastomeres, evenness, and fragmentation percentage. Day 5 grading criteria included the inner cell mass, trophectoderm, and blastocyst stage evaluations. Participants/materials, setting, methods An AI model using latent diffusion was developed to generate 10,051 Day 5 and 10,088 Day 3 synthetic embryo images. The authenticity was assessed through visual Turing tests, where embryologists discerned real from synthetic images. For the evaluation, 200 real (100 Day 5, 100 Day 3) and 200 synthetic (100 Day 5, 100 Day 3) images were randomly chosen from each dataset, ensuring a comprehensive test of the generated images’ realism. Main results and the role of chance The AI model’s efficacy in generating synthetic embryo images was assessed through visual Turing tests on Day 5 and Day 3 embryos, yielding accuracies of 0.59 and 0.57, respectively. These accuracies fall within the 99% confidence interval of near-random performance (0.41 to 0.59), highlighting the challenge in distinguishing synthetic from real images. For Day 5 embryos, the sensitivity and specificity were recorded at 0.52 and 0.66, indicating a moderate challenge in identifying synthetic images and a relatively higher ease in recognizing real ones. Day 3 embryos presented a lower sensitivity of 0.41, suggesting greater difficulty in detecting synthetic images, while the specificity increased to 0.73, indicating a stronger ability to identify real images. Collectively, with an overall accuracy of 0.58, sensitivity of 0.47, and specificity of 0.70, these findings confirm the synthetic images’ remarkable realism, closely emulating actual embryo features to the extent that differentiation by experts proved challenging. This level of realism underscores the synthetic images’ potential to significantly enrich embryological research datasets, promising advancements in the field. Limitations, reasons for caution The study’s limitation lies in the generated synthetic embryos being based on three embryo grade categories, limiting feature diversity. Additionally, there was no quantitative assessment method for these images, relying instead on expert evaluation, which required deploying a large number of embryologists. Wider implications of the findings The model’s generation of highly realistic embryo images counters data scarcity in embryological research, potentially elevating AI’s utility, enriching educational content, advancing reproductive medicine, and ensuring ethical data usage. Trial registration number not applicable

O-260 Identifying seminiferous tubules for sperm retrieval during microdissection testicular sperm extraction (M-TESE) using deep learning

Abstract Study question Is it possible to utilize artificial intelligence (AI) in the selection of seminiferous tubules for sperm retrieval during microdissection testicular sperm extraction (M-TESE)? Summary answer AI algorithm was able to segment seminiferous tubules deemed to be good candidates for sperm retrieval with IoU of 0.746 and F1 score of 72.5%. What is known already M-TESE is an advanced surgical procedure reserved for sperm retrieval in men with non-obstructive azoospermia (NOA). Meta-analysis have shown M-TESE might be superior in relation to sperm retrieval rates and postsurgical complications when compared to conventional techniques. Limitations of the technique are the relatively long learning curve for the surgeons and the longer intraoperative time required to identify the tubules with active spermatogenesis. Convolutional neural networks (CNNs) have been shown to successfully identify areas of interest in medical images such as CT and MRI scans. The possibility of utilizing trained CNNs in M-TESE procedures has so far not been studied. Study design, size, duration This is a cross sectional study analyzing image data gathered during M-TESE procedures including the first five men enrolled in a prospective study designed specifically to test the utilization of AI in the treatment of NOA. All participants signed written informed consent. Images were collected intraoperatively with low and high resolution cameras according to predefined protocol. Participants/materials, setting, methods CNN model with down and upsampling architecture was trained using patch-based technique with corresponding labelled truth image pairs based on combination of semi-automated hue saturation value thresholding and manual segmentation of seminiferous tubules. Tubules were allocated into two different classes by the operating surgeon according to the visual perception of their morphology as type 1 (i.e. larger, opaque, good candidates for micro dissection) and type 2 (i.e. sclerotic or atrophic; unlikely to harbor active spermatogenesis). Main results and the role of chance CNN models were trained on 1809 patches with corresponding ground truth segmented images. Two algorithms were tested: the first was trained solely to detect and semantically distinguish type 1 seminiferous tubules. The second was trained to semantically segment the major anatomical structures: tunica albuginea, testicular parenchyma, blood vessels; lobules and the two types of seminiferous tubules describe above. Validation was performed on 988 image patches not previously shown to the algorithm. After grid search for optimal learning rate and augmentation the first algorithm was able to segment type 1 tubules with F1 score of 72.5% and Jaccard Index (IoU) of 0.746. Training took 856 minutes on 48 core high performance computer (HPC) equipped with single graphics processing unit (GPU). Same computational environment was used in the training of the second algorithm. This model was able to segment anatomical structures such as parenchyma (IoU=0.732) and testicular lobules (IoU=0.683) but performed sub optimally on the clinically relevant type 1 tubules (IoU=0.432). The first trained algorithm was able to segment a single high resolution surgical image in 3.4 seconds on an HPC. However, the same procedure took 5.5 minutes on standard desktop computer with 8 core Intel processor without GPU. Limitations, reasons for caution The number of patients is relatively small and therefore more training data would be needed to achieve robust performance. Training data were labelled by single surgeon limiting the generalizability and possibly biasing the selection process. The need of HPC environment for real time segmentation can limit the clinical use. Wider implications of the findings This pilot study shows the potential of AI to help in choosing suitable tubules for micro dissection, shortening the learning curve for young andrological surgeons. The access to cluster based computation is getting easier by the day and can soon allow instant intraoperative AI segmentation, thus shortening the surgical procedure. Trial registration number NA

Suppressing Protodeboronation in Cu-Mediated 19F/18F-Fluorination of Arylboronic Acids: A Mechanistically Guided Approach Towards Optimized PET Probe Development.

Fluorinated arenes play a crucial role in drug discovery, specialty materials, and medical imaging. Although several variants for Cu-mediated nucleophilic fluorination of arylboronic acids and derivatives have been developed, these protocols rarely address the occurrence and control of protodeboronation, which greatly complicates product separation and can compromise the effectiveness of a radiotracer for in vivo imaging. Consequently, simpler and more efficient procedures are needed to allow rapid 18F/19F-fluorination of both arylboronic acids and esters while minimizing protodeboronation. Mechanistic controls revealed that in addition to a high temperature, strong donor ligands such as acetonitrile and pyridine accentuate a Cu-mediated protodeboronation. This observation guided the optimization of a ligandless procedure with t-BuOH as solvent to enhance the nucleophilicity of fluoride under milder conditions at lower temperatures minimizing protodeboronation. Additionally, a new copper salt, Cu(ONf)2 was employed to further improve the fluorination efficiency. A large range of functional groups are tolerated under the new procedure, which is complete within 30 minutes at a temperature of 60 °C, and affords fluorinated arenes and heteroarenes in 39% to 84% yield. With minimal modifications, the protocol can also be applied in 18F-radiofluorination, affording radiochemical conversions (RCCs) between 17-54% with minimal protodeboronation compared to previously established protocols.

280 Validating Artificial Intelligence Generated Patient Information Leaflets: A Patient Survey

Abstract Aim Artificial Intelligence (AI) is a ground-breaking technological advancement known for its prowess in processing extensive datasets, gaining recognition among clinicians for its potential in improving patient care. While AI is successfully employed in analysing medical imaging, its use in developing patient information literature (PIL) is limited. Patient access to information aligns with modern healthcare values, emphasizing shared decision-making, a priority in the NHS. This study, conducted under single-blinded conditions, explores the acceptability of AI-generated patient documentation. Method Four PIL examples related to common ENT procedures and conditions were identified. ChatGPT 3.5 modified existing PILs, adjusting for a UK reading age of 14. AI-generated PILs were formatted to match trust documents. Participants were blinded to leaflet sources. Results In 59 responses from ENT outpatients over six weeks, both AI and Trust leaflets had comparable appearance scores (AI 6.82, Trust 6.85). Although a slight preference for Trust leaflets existed, it wasn't statistically significant (p=0.47). In overall presentation preference, most participants had no preference (N=27), with one-third favoring Trust leaflets and 13 preferring AI-generated ones. In explaining conditions, over half had no preference (N=36), while 16 favored human-generated and 7 AI-generated leaflets. Readability and understanding were similar, with 25 participants finding both equally easy to read. Overall satisfaction was comparable, with 26 equally satisfied, 18 preferring human-generated, and 15 AI-generated leaflets. Conclusions AI technology integrated into clinical practice holds potential to transform PIL quality and quantity. As AI accessibility increases, healthcare services must guide the public to safeguard the standard of provided information.

A 2.5D Self-Training Strategy for Carotid Artery Segmentation in T1-Weighted Brain Magnetic Resonance Images

Precise annotations for large medical image datasets can be time-consuming. Additionally, when dealing with volumetric regions of interest, it is typical to apply segmentation techniques on 2D slices, compromising important information for accurately segmenting 3D structures. This study presents a deep learning pipeline that simultaneously tackles both challenges. Firstly, to streamline the annotation process, we employ a semi-automatic segmentation approach using bounding boxes as masks, which is less time-consuming than pixel-level delineation. Subsequently, recursive self-training is utilized to enhance annotation quality. Finally, a 2.5D segmentation technique is adopted, wherein a slice of a volumetric image is segmented using a pseudo-RGB image. The pipeline was applied to segment the carotid artery tree in T1-weighted brain magnetic resonance images. Utilizing 42 volumetric non-contrast T1-weighted brain scans from four datasets, we delineated bounding boxes around the carotid arteries in the axial slices. Pseudo-RGB images were generated from these slices, and recursive segmentation was conducted using a Res-Unet-based neural network architecture. The model’s performance was tested on a separate dataset, with ground truth annotations provided by a radiologist. After recursive training, we achieved an Intersection over Union (IoU) score of (0.68 ± 0.08) on the unseen dataset, demonstrating commendable qualitative results.

Medical Image Classification using a Many to Many Relation, Multilayered Fuzzy Systems and AI

One of the research gaps in the medical sciences is the study of orphan diseases or rare diseases, due to limited data availability of rare diseases. Our previous study addressed this successfully by developing an Artificial Intelligence (AI)-based medical image classification method using a multilayer fuzzy approach (MFA), for detecting and classifying image abnormalities for large and very small datasets. A fuzzy system is an AI system used to handle imprecise data. There are more than three types of fuzziness in any image data set: 1) due to a projection of a 3D object on a 2D surface, 2) due to the digitalization of the scan, and 3) conversion of the digital image to grayscale, and more. Thus, this was referred to in the previous study as a multilayer fuzzy system, since fuzziness arises from multiple sources. The method used in MFA involves comparing normal images containing abnormalities with the same kind of image without abnormalities, yielding a similarity measure percentage that, when subtracted from a hundred, reveals the abnormality. However, relying on a single standard image in the MFA reduces efficiency, since images vary in contrast, lighting, and patient demographics, impacting similarity percentages. To mitigate this, the current study focused on developing a more robust medical image classification method than MFA, using a many-to-many relation and a multilayer fuzzy approach (MCM) that employs multiple diverse standard images to compare with the abnormal image. For each abnormal image, the average similarity was calculated across multiple normal images, addressing issues encountered with MFA, and enhancing versatility. In this study, an AI-based method of image analysis automation that utilizes fuzzy systems was applied to a cancer data set for the first time. MCM proved to be highly efficient in detecting the abnormality in all types of images and sample sizes and surpassed the gold standard, the convolutional neural network (CNN), in detecting the abnormality in images from a very small data set. Moreover, MCM detects and classifies abnormality without any training, validation, or testing steps for large and small data sets. Hence, MCM may be used to address one of the research gaps in medicine, which detects, quantifies, and classifies images related to rare diseases with small data sets. This has the potential to assist a physician with early detection, diagnosis, monitoring, and treatment planning of several diseases, especially rare diseases.

Improved security of medical images using DWT–SVD watermarking mechanisms based on firefly Photinus search algorithm

Security is the primary concern in the transmission of medical images, as it involves sensitive patient information. This study introduces an optimized watermarking approach, constructed using discrete wavelet transform and singular value decomposition. The Low- level frequency bands (LL3) sub-band singular values of the host image were embedded with the singular values of a binary watermark using multiple scaling factors. These MSFs were optimized using a recently proposed firefly Photinus algorithm to balance robustness and imperceptibility. The proposed method was applied to various images, including computed tomography images, where the visual quality of the signed and attacked images was evaluated by peak signal-to-noise ratio (PSNR) and normalized cross-correlation. The performance of the proposed algorithm demonstrates significant improvements in the embedding and extraction processes, showing an enhancement in the balance between robustness and imperceptibility, with a PSNR above 79.28 dB, compared to other related works.

Role of Bowel Hydrosonography in Evaluation of Inflammatory Bowel Diseases Compared to CT or MR Enterography

Abstract Background bowel diseases (IBD). CT entrography (CTE), bowel US, magnetic resonance enterography Crohn’s disease and ulcerative colitis are chronic, relapsing inflammatory (MRE) are decisive medical imaging modalities for diagnosing and locating bowel lesions with its extramural extent and complications. They assess the degree of activity, help clinicians to identify patients in need of surgery, and can be used for patient follow-up. Aim To evaluate the utility of small bowel hydrosonography (SBH) in assessment if patient s with proven IBD in comparison to the currently available CT/MR enterographic techniques. Methods The study was conducted on 41 patients with histologically proven IBD. All patients were subjected to clinical assessment, laboratory investigations, MRE/CTE then bowel ultrasound. Results This study was conducted on 41 patients (19.5%) with ulcerative colitis and (80.5 %) with Crohn's disease; Terminal illeum is the most affected detected segment (22.1%) by bowel ultrasound and(22.5%) by CTE/MRE, ultrasound also showed statistically significance results in detection evidence of complicated Crohn’s disease such as fistula and abscess. Conclusion In comparison to MRE/CTE, bowel hydrosonography is a useful, non- invasive, and feasible bedside imaging tool for the detection of inflammation, detection of complications, and follow-up of IBD patients.

Colon cancer diagnosis by means of explainable deep learning

Early detection of the adenocarcinoma cancer in colon tissue by means of explainable deep learning, by classifying histological images and providing visual explainability on model prediction. Considering that in recent years, deep learning techniques have emerged as powerful techniques in medical image analysis, offering unprecedented accuracy and efficiency, in this paper we propose a method to automatically detect the presence of cancerous cells in colon tissue images. Various deep learning architectures are considered, with the aim of considering the best one in terms of quantitative and qualitative results. As a matter of fact, we consider qualitative results by taking into account the so-called prediction explainability, by providing a way to highlight on the tissue images the areas that from the model point of view are related to the presence of colon cancer. The experimental analysis, performed on 10,000 colon issue images, showed the effectiveness of the proposed method by obtaining an accuracy equal to 0.99. The experimental analysis shows that the proposed method can be successfully exploited for colon cancer detection and localisation from tissue images.

XRAInet: AI-based decision support for pneumothorax and pleural effusion management.

This study aimed to develop and assess the performance of an artificial intelligence(AI)-driven decision support system, XRAInet, in accurately identifying pediatric patients with pleural effusion or pneumothorax and determining whether tube thoracostomy intervention is warranted. In this diagnostic accuracy study, we retrospectively analyzed a data set containing 510 X-ray images from 170 pediatric patients admitted between 2005 and 2022. Patients were categorized into two groups: Tube (requiring tube thoracostomy) and Conservative (managed conservatively). XRAInet, a deep learning-based algorithm, was trained using this data set. We evaluated its performance using various metrics, including mean Average Precision (mAP), recall, precision, and F1 score. XRAInet, achieved a mAP score of 0.918. This result underscores its ability to accurately identify and localize regions necessitating tube thoracostomy for pediatric patients with pneumothorax and pleural effusion. In an independent testing data set, the model exhibited a sensitivity of 64.00% and specificity of 96.15%. In conclusion, XRAInet presents a promising solution for improving the detection and decision-making process for cases of pneumothorax and pleural effusion in pediatric patients using X-ray images. These findings contribute to the expanding field of AI-driven medical imaging, with potential applications for enhancing patient outcomes. Future research endeavors should explore hybrid models, enhance interpretability, address data quality issues, and align with regulatory requirements to ensure the safe and effective deployment of XRAInet in healthcare settings.