Computer-aided System Research Articles

Enhancing detection of various pancreatic lesions on endoscopic ultrasound through artificial intelligence: a basis for computer-aided detection systems.

Endoscopic ultrasound (EUS) is the most sensitive method for evaluation of pancreatic lesions but is limited by significant operator dependency. Artificial intelligence (AI), in the form of computer-aided detection (CADe) systems, has shown potential in increasing accuracy and bridging operator dependency in several endoscopic domains. However, the complexity of integrating AI into EUS is far more challenging. This aims to develop and test the basis for a CADe system for real-time detection and segmentation of all pancreatic lesions. In this single-center study EUS studies of pancreatic findings were included. Lesions were outlined by two expert (>5years performing EUS) endoscopists, and the two leading types of models were benchmarked. The models' performance was evaluated through per-pixel intersection over union (IoU). A total of 1497 EUS images from 165 patients were evaluated. The dataset included malignancies, neuroendocrine tumors, benign cysts, chronic and acute pancreatitis, normal fatty pancreas, and benign lesions. The best model demonstrated detection and segmentation on the test set with a mean IoU of 0.73, achieving a PPV, NPV, total accuracy, and ROC of 0.82, 0.96, 0.95, and 0.95, respectively. The algorithm is adaptable for real-time processing. We developed and tested deep learning models for real-time detection and segmentation of pancreatic lesions during EUS with promising results. This constitutes the basis for a CADe system for EUS, which could be valuable in future detection and evaluation of pancreatic lesions. Further studies for validation and generalization are underway.

Effect of different corrective force directions applied by spinal orthoses on the patients with adolescent idiopathic scoliosis.

Spinal orthoses are commonly prescribed for adolescent idiopathic scoliosis (AIS), yet their three-dimensional correction was not fully understood. The amount of deformity control largely depends on the corrective forces applied, which remain empirically based due to a lack of consensus on optimal force application. This study investigated the effects of different corrective force directions exerted by spinal orthoses on patients with AIS. A retrospective analysis was conducted on 78 subjects. The trunk was segmented into four quadrants using coronal and sagittal planes from a top-down perspective. Each left or right posterolateral quadrant (with 90°) was further subdivided into zones 1-4, from the sagittal to coronal planes. Based on the zone where the resultant corrective force direction fell, the subjects were categorized into Group 1 (zone 1), Group 2 (zone 2), Group 3 (zone 3), or Group 4 (zone 4). The direction of the corrective force was estimated using modified models of the subjects' bodies, designed through a computer-aided design and manufacturing system integral to the orthosis fabrication process. The effects of corrective forces in different zones on scoliotic spine were assessed. Among the subjects, 3 were in Group 1, 17 in Group 2, 52 in Group 3, and 6 in Group 4. Due to the limited number of subjects, data from Groups 1 and 4 were not analysed. Groups 2 and 3 showed significant reductions in Cobb angle in the coronal plane and plane of maximum curvature (PMC) following orthosis fitting (p < 0.05). Group 2 displayed a significant decrease > 5º in thoracic kyphosis (p < 0.05). Both Groups 2 and 3 exhibited significant reductions in lumbar lordosis. PMC orientation remained unchanged over time (p > 0.05) but was notably higher in Group 2 after orthosis fitting (p < 0.05). Corrective forces applied by spinal orthoses in zones 2 and 3 effectively controlled lateral curve progression. Notably, only forces in zone 3 neither significantly reduced thoracic kyphosis nor exacerbated the deviation of scoliotic spine from the sagittal plane. Further research is needed to validate and expand upon these results.

A Computer Aided System for Reservoir Rock Classification

This study is mainly aimed to know the reservoir rocks among core specimens that are obtained during the stage of drilling of exploration oil and gas wells by the processing of the image digitally using a Computer-Aided System. This computerized method has been conducted using two programming languages; Python and Matlab. The Python program is used to segment rock images and classify the extracted features, whereas the MATLAB language has been used to extract intensity features from segmented images. The database used in this project has been collected from two sources, one of them from the Sarier formation (the biggest Libyan oil field) while the second source was from the internet. The images used in this study are sedimentary rocks images, which include sandstone, limestone, and dolomite. As a result, this study has reached that the system, which was used here is capable of classifying rocks and can be used to classify oil rocks or any other type of rocks. Also, it can be used near the well due to certain conditions such as the absence of experts due to the lack of enough database, the study has only introduced the preliminary results as proof of the concept.

The CFO financial performance and the financial market scenario: A Review

This article introduces a literature review on the CFO's financial performance in the ever-challenging Brazilian financial market scenario. Key findings pointed out that the CFO of unlisted companies shows a lower inclination to estimate the cost of equity. The study also highlighted the need for technical and organizational solutions to agency problems, such as sound corporate governance practices, computer-aided accounting systems, incentive-based compensation, clear performance goals, and delegation of operational decisions. The study also examined the CFO's perspective and the companies' strategic ecosystem, finding that duality in the roles can negatively impact financial reporting quality.

Machine Learning Based Decision Support System for the Diagnosis of Breast Cancer

Breast cancer is among the most prevalent diseases encountered among women worldwide. Early diagnosis of breast cancer is crucial for the treatment of the disease. Detecting the disease at an early stage prevents deaths resulting from the condition. Recently, computer-aided systems have been developed to ensure early-stage diagnosis and accuracy of breast cancer. Computer-aided systems developed with machine learning approaches significantly contribute to the process of diagnosing breast cancer. The aim of this study is to propose a new classification system based on machine learning algorithms developed for the diagnosis of breast cancer. In this study, sub-data sets were created by reducing features, and data cleaning processes were applied. After these procedures, stages such as feature selection and feature extraction were applied. In this study, classification processes such as Ensemble, k- Nearest Neighbors (kNN), Support Vector Machines (SVMs), and Hybrid Artificial Intelligence were used in line with machine learning. With the obtained results, a Breast Cancer diagnosis algorithm was created. Performance evaluation criteria such as accuracy rate, specificity, sensitivity, kappa number and F-Measure were applied to the created algorithms. In the results obtained in this study, the highest accuracy rate was found to be 99.3% with the Ensemble method, the highest specificity rate was 98.7% with the Ensemble method, and the highest sensitivity rate was found to be 100% with many methods. In light of these results, it was observed that the machine learning algorithms used in this study, implemented in the Matlab environment, were effective. Consequently, it was proven that higher accuracy, specificity, and sensitivity rates can be found with different machine learning techniques. This also demonstrates that the study in our article is a reliable one in detecting diseased and healthy individuals in the diagnosis of breast cancer, showing that it is a more applicable and feasible study in the healthcare field.

Part-Prototype Models in Medical Imaging: Applications and Current Challenges

Recent developments in Artificial Intelligence have increasingly focused on explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided diagnosis systems and its usage for knowledge discovery are gaining interest in the medical imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI are focused on interpreting the predictions made using deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This highlights the importance of proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process on learning and identifying representative prototypical parts from input images, and they are gaining increasing interest and results in MI applications. However, the medical field has unique characteristics that could benefit from more advanced implementations of these types of architectures. This narrative review summarizes existing PP networks, their application in MI analysis, and current challenges.

Deep Learning With Optical Coherence Tomography for Melanoma Identification and Risk Prediction.

Malignant melanoma is the most severe skin cancer with a rising incidence rate. Several noninvasive image techniques and computer-aided diagnosis systems have been developed to help find melanoma in its early stages. However, most previous research utilized dermoscopic images to build a diagnosis model, and only a few used prospective datasets. This study develops and evaluates a convolutional neural network (CNN) for melanoma identification and risk prediction using optical coherence tomography (OCT) imaging of mice skin. Longitudinal tests are performed on four animal models: melanoma mice, dysplastic nevus mice, and their respective controls. The CNN classifies melanoma and healthy tissues with high sensitivity (0.99) and specificity (0.98) and also assigns a risk score to each image based on the probability of melanoma presence, which may facilitate early diagnosis and management of melanoma in clinical settings.

Comparative Bladder Cancer Tissues Prediction Using Vision Transformer.

Bladder cancer, often asymptomatic in the early stages, is a type of cancer where early detection is crucial. Herein, endoscopic images are meticulously evaluated by experts, and sometimes even by different disciplines, to identify tissue types. It is believed that the time spent by experts can be utilized for patient treatment with the creation of a computer-aided decision support system. For this purpose, in this study, it is evaluated that the performances of three models proposed using the bladder tissue dataset. The first model is a convolutional neural network (CNN)-based deep learning (DL) network, and the second is a model named hybrid cnn-machine learning (ML) or DL + ML, which involves classifying deep features obtained from a CNN-based network with ML. The last one, and the one that achieved the best performance metrics, is a vision transformer (ViT) architecture. Furthermore, a graphical user interface (GUI) is provided for an accessible decision support system. As a result, accuracy and F1 score values for DL, DL + ML, and ViT models are 0.9086-0.8971-0.9257 and 0.8884-0.8496-0.8931, respectively.

Association between Radiological and First-Order Statistical Features of the Mammogram, and the Tumor Phenotype in Breast Cancer Patients

Background: Breast cancer is among the most prevalent cancers which can effectively be screened by mammography. The spatial distribution of grey levels of the mammogram known as first-order statistical features (FOSFs) contain higher dimensional data which describe the breast composition. We aim to test these basic measures to differentiate density categories in breast cancer patients, and use them as covariates to investigate the relationship between radiologic and pathologic features of the tumor. Methods: Data from Eighty-five breast cancer subjects, their BI-RADS breast density category (a to d), percentage density (PD), and FOSFs of the mammogram including median, mean, interquartile range, kurtosis, maximum, minimum, standard deviation, skewness, and energy, were extracted. The tumor grade and the percentage of Ki67, ER, PR, and Her2 status were recorded. A linear discriminant analysis, and a support vector machine (SVM) were used to discriminate each density category from others. Then, the relation between variables were investigated using ANCOVA and regression analysis. Results: Density categories a and d were classified by SVM with high accuracy. The key feature of a and d were interquartile range and maximum intensities respectively. Reported tumor margins were related to Her2 overexpression and PR positivity. Spiculated tumor margin predicted percentage of PR expression, with a cumulative odds ratio of 7.85 (CI 2.5- 24.78), when adjusted for age, area of breast, density, and FOSFs, (p=0.0004). Conclusion: The findings of this study suggest that FOSFs can be incorporated in computer-aided systems to adjust for differences in breast composition and to refine risk profiles.

A New Computer-Aided Diagnosis System for Breast Cancer Detection from Thermograms Using Metaheuristic Algorithms and Explainable AI

Advances in the early detection of breast cancer and treatment improvements have significantly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study, a novel interpretable computer aided diagnosis (CAD) system for breast cancer detection is proposed, leveraging Explainable Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named the Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid Spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combined the continuous and binary representations of PSO and SMO to effectively manage trade-offs between accuracy, feature selection, and hyperparameter tuning. We evaluated several CAD models and investigated the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor Filters, and Edge Detection. We further shed light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. With HSMO, our models achieved an accuracy of 98.27% and F1-score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis.

A 3-year clinical evaluation of endocrown restorations with two different materials using the computer-aided design/ computer-aided manufacture system

ObjectiveTo compare the clinical efficacy of resin nanoceramic and zirconia-reinforced lithium silicate ceramic endocrowns in the treatment of endodontically treated teeth by assessing the clinical performance of restorations fabricated using a chairside computer-aided design/computer-aided manufacturing (CAD/CAM) system. Materials and methodsNinety endocrown restorations were fabricated on endodontically treated teeth using a CAD/CAM system. Fifty-two restorations were fabricated using Lava Ultimate blocks (LU, 3 M Espe, Seefeld, Germany) and 38 restorations were fabricated using Celtra Duo blocks (CD, Dentsply Sirona, York, PA, USA). The performance of the endocrown restorations was evaluated over a 3-year period using the modified United States Public Health Service (USPHS) criteria by two calibrated evaluators. The data were analyzed using Chi-square (x2) and Fisher exact tests (p < 0.05). ResultsThe restoration survival rates in the two groups were 82.7 % (LU) and 86.8 % (CD) (p = 0.6). Twelve restorations failed (8 LU, 4 CD) due to debonding (n = 4, 2 LU, 2 CD), ceramic fracture (n = 2, 1 LU, 1 CD), tooth fracture (n = 5, 4 LU, 1 CD), and secondary caries (n = 1, 1 LU) during the follow-up period. There was no statistically significant difference between the two materials according to the modified USPHS criteria (p > 0.05). ConclusionResin nanoceramic and zirconia-reinforced lithium silicate ceramic endocrown restorations fabricated using a chairside CAD/CAM system demonstrated acceptable clinical performance in the treatment of endodontically treated teeth. Clinical SignificanceResin nanoceramic and zirconia-reinforced lithium silicate ceramics are potential alternative materials for the chairside fabrication of endodontically treated teeth.

A Modified Deep Semantic Segmentation Model for Analysis of Whole Slide Skin Images

Automated segmentation of biomedical image has been recognized as an important step in computer-aided diagnosis systems for detection of abnormalities. Despite its importance, the segmentation process remains an open challenge due to variations in color, texture, shape diversity and boundaries. Semantic segmentation often requires deeper neural networks to achieve higher accuracy, making the segmentation model more complex and slower. Due to the need to process a large number of biomedical images, more efficient and cheaper image processing techniques for accurate segmentation are needed. In this article, we present a modified deep semantic segmentation model that utilizes the backbone of EfficientNet-B3 along with UNet for reliable segmentation. We trained our model on Non-melanoma skin cancer segmentation for histopathology dataset to divide the image in 12 different classes for segmentation. Our method outperforms the existing literature with an increase in average class accuracy from 79 to 83%. Our approach also shows an increase in overall accuracy from 85 to 94%.

Improving the diagnostic strategy for thyroid nodules: a combination of artificial intelligence-based computer-aided diagnosis system and shear wave elastography.

Thyroid nodules are highly prevalent in the general population, posing a clinical challenge in accurately distinguishing between benign and malignant cases. This study aimed to investigate the diagnostic performance of different strategies, utilizing a combination of a computer-aided diagnosis system (AmCAD) and shear wave elastography (SWE) imaging, to effectively differentiate benign and malignant thyroid nodules in ultrasonography. A total of 126 thyroid nodules with pathological confirmation were prospectively included in this study. The AmCAD was utilized to analyze the ultrasound imaging characteristics of the nodules, while the SWE was employed to measure their stiffness in both transverse and longitudinal thyroid scans. Twelve diagnostic patterns were formed by combining AmCAD diagnosis and SWE values, including isolation, series, parallel, and integration. The diagnostic performance was assessed using the receiver operating characteristic curve and area under the curve (AUC). Sensitivity, specificity, accuracy, missed malignancy rate, and unnecessary biopsy rate were also determined. Various diagnostic schemes have shown specific advantages in terms of diagnostic performance. Overall, integrating AmCAD with SWE imaging in the transverse scan yielded the most favorable diagnostic performance, achieving an AUC of 72.2% (95% confidence interval (CI): 63.0-81.5%), outperforming other diagnostic schemes. Furthermore, in the subgroup analysis of nodules measuring <2 cm or 2-4 cm, the integrated scheme consistently exhibited promising diagnostic performance, with AUCs of 74.2% (95% CI: 61.9-86.4%) and 77.4% (95% CI: 59.4-95.3%) respectively, surpassing other diagnostic schemes. The integrated scheme also effectively addressed thyroid nodule management by reducing the missed malignancy rate to 9.5% and unnecessary biopsy rate to 22.2%. The integration of AmCAD and SWE imaging in the transverse thyroid scan significantly enhances the diagnostic performance for distinguishing benign and malignant thyroid nodules. This strategy offers clinicians the advantage of obtaining more accurate clinical diagnoses and making well-informed decisions regarding patient management.

Multi-label semantic segmentation of magnetic resonance images of the prostate gland

Prostate segmentation is a substantial factor in the diagnostic pathway of suspicious prostate lesions. Medical doctors are assisted by computer-aided detection and diagnosis systems systems and methods derived from artificial intelligence deep learning-based systems have to be trained on existing data. Especially freely available labeled prostate magnetic resonance imaging data is rare. With regard to this problem, we show a method to combine two existing small datasets to form a bigger one. We present a data processing pipeline consisting of a cascaded network architecture that is able to perform multi-label semantic segmentation, hence, capable of classifying each pixel in a T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{T}_{2}$$\\end{document}-weighted image not only to one class but to a subset of the prostate zones and classes of interest. This delivers richer information such as overlapping zones that are key to medical radiological examination. Additionally, we describe how to integrate expert knowledge in our deep learning system. To increase data variety for training and evaluation we use image augmentation on our two datasets—a freely available dataset and our new open-source dataset. To combine the datasets we denoise the contourings in our dataset by using an effective yet simple algorithm based on standard computer vision methods only. The performance of the presented methodology is compared and evaluated using the dice score metric and 5-fold cross-validation on all datasets. Although we trained on tiny datasets our method achieves excellent segmentation quality and is even able to detect prostate cancer. Our method to combine the two datasets reduces segmentation errors and increases data variety. The proposed architecture significantly improves performance by including expert knowledge via feature-map concatenation. On the initiative for collaborative computer vision benchmarking dataset we achieve on average dice scores of approximately 91% for the whole prostate gland, 67% for the peripheral zone and 75% for the prostate central gland. We find that image augmentation except contrast limited adaptive histogram equalisation did not have much influence on the segmentation quality. Derived and enhanced from existing methods we present an approach that is able to deliver multi-label semantic segmentation results for prostate magnetic resonance imaging. This approach is simple and could be applied to other applications of deep learning as well. It improves the segmentation results by a large margin. Once tweaked to the data, our denoising and combination algorithm delivers robust and accurate results even on data with segmentation errors.

RFMiD: Retinal Image Analysis for multi-Disease Detection challenge

In the last decades, many publicly available large fundus image datasets have been collected for diabetic retinopathy, glaucoma, and age-related macular degeneration, and a few other frequent pathologies. These publicly available datasets were used to develop a computer-aided disease diagnosis system by training deep learning models to detect these frequent pathologies. One challenge limiting the adoption of a such system by the ophthalmologist is, computer-aided disease diagnosis system ignores sight-threatening rare pathologies such as central retinal artery occlusion or anterior ischemic optic neuropathy and others that ophthalmologists currently detect. Aiming to advance the state-of-the-art in automatic ocular disease classification of frequent diseases along with the rare pathologies, a grand challenge on “Retinal Image Analysis for multi-Disease Detection” was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2021). This paper, reports the challenge organization, dataset, top-performing participants solutions, evaluation measures, and results based on a new “Retinal Fundus Multi-disease Image Dataset” (RFMiD). There were two principal sub-challenges: disease screening (i.e. presence versus absence of pathology — a binary classification problem) and disease/pathology classification (a 28-class multi-label classification problem). It received a positive response from the scientific community with 74 submissions by individuals/teams that effectively entered in this challenge. The top-performing methodologies utilized a blend of data-preprocessing, data augmentation, pre-trained model, and model ensembling. This multi-disease (frequent and rare pathologies) detection will enable the development of generalizable models for screening the retina, unlike the previous efforts that focused on the detection of specific diseases.

Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding

Objective. Identifying the seizure occurrence period (SOP) in extended EEG recordings is crucial for neurologists to diagnose seizures effectively. However, many existing computer-aided diagnosis systems for epileptic seizure detection (ESD) primarily focus on distinguishing between ictal and interictal states in EEG recordings. This focus has limited their application in clinical settings, as these systems typically rely on supervised learning approaches that require labeled data.Approach. To address this, our study introduces an unsupervised learning framework for ESD using a 1D- cascaded convolutional autoencoder (1D-CasCAE). In this approach, EEG recordings from selected patients in the CHB-MIT datasets are first segmented into 5 s epochs. Eight informative channels are chosen based on the correlation coefficient and Shannon entropy. The 1D-CasCAE is designed to autonomously learn the characteristic patterns of interictal (non-seizure) segments through downsampling and upsampling processes. The integration of adaptive thresholding and a moving window significantly enhances the model's robustness, enabling it to accurately identify ictal segments in long EEG recordings.Main results. Experimental results demonstrate that the proposed 1D-CasCAE effectively learns normal EEG signal patterns and efficiently detects anomalies (ictal segments) using reconstruction errors. When compared with other leading methods in anomaly detection, our model exhibits superior performance, as evidenced by its average Gmean, sensitivity, specificity, precision, and false positive rate scores of 98.00% ± 3.51%, 94.94% ± 6.92%, 99.60% ± 0.30%, 79.92% ± 13.56% and 0.0044 ± 0.0030 h-1respectively for a typical patient in CHB-MIT datasets.Significance. The developed model framework can be employed in clinical settings, replacing the manual inspection process of EEG signals by neurologists. Furthermore, the proposed automated system can adapt to each patient's SOP through the use of variable time windows for seizure detection.

Computer-Assisted Online Learning of English Oral Pronunciation Based on DAE End-to-End Recurrent Neural Networks

With the development of globalization, learning a second language has received increasing attention from people. To improve English oral proficiency, a computer-aided online learning system for English oral pronunciation is studied. A denoising autoencoder is integrated into the system to create a simplified end-to-end recurrent neural network for pronunciation detection and diagnosis based on deep learning. The study first collected and preprocessed oral pronunciation data of English learners, including enhancing speech signals and reducing noise. Next, an RNN model with Long Short-Term Memory (LSTM) as the core was constructed to capture time series characteristics in pronunciation. And use DAE to extract features and reduce the influence of background noise to enhance the recognition of pronunciation features. At the same time, the study utilized web crawler technology to collect a large amount of oral pronunciation data from non-native English learners, and constructed an English oral corpus containing pronunciation errors. And in order to simulate real situations, white noise and pink noise were artificially added to the corpus in the study, and they were divided into training and testing sets in a ratio of 60% to 40%. The results showed that the classification accuracy of the system in the training and testing sets under white noise environment was 78.97% and 94.01%, respectively, and the classification accuracy in the pink noise environment was 76.19% and 94.03%, respectively. The system’s error detection accuracy in vowel and consonant pronunciation detection is 88.91% and 91.68%, respectively, and the error correction accuracy in vowel and consonant pronunciation detection is 90.67% and 91.96%, respectively. In summary, the research on computer-aided online learning of English oral pronunciation based on Denoising Auto Encoders end-to-end recurrent neural networks has effectively improved learning efficiency.

Formalization of topology and electrical circuit verification for computer-aided design systems

Formalization of topology and electrical circuit verification for computer-aided design systems

S372 Evaluating the Performance of a Computer-Aided Diagnosis System in Implementing Diagnose-and-Leave and Resect-and-Discard Strategies for Diminutive Colorectal Polyps: A Real-World Pragmatic Study

S372 Evaluating the Performance of a Computer-Aided Diagnosis System in Implementing Diagnose-and-Leave and Resect-and-Discard Strategies for Diminutive Colorectal Polyps: A Real-World Pragmatic Study

The Integration of Radiomics and Artificial Intelligence in Modern Medicine.

With profound effects on patient care, the role of artificial intelligence (AI) in radiomics has become a disruptive force in contemporary medicine. Radiomics, the quantitative feature extraction and analysis from medical images, offers useful imaging biomarkers that can reveal important information about the nature of diseases, how well patients respond to treatment and patient outcomes. The use of AI techniques in radiomics, such as machine learning and deep learning, has made it possible to create sophisticated computer-aided diagnostic systems, predictive models, and decision support tools. The many uses of AI in radiomics are examined in this review, encompassing its involvement of quantitative feature extraction from medical images, the machine learning, deep learning and computer-aided diagnostic (CAD) systems approaches in radiomics, and the effect of radiomics and AI on improving workflow automation and efficiency, optimize clinical trials and patient stratification. This review also covers the predictive modeling improvement by machine learning in radiomics, the multimodal integration and enhanced deep learning architectures, and the regulatory and clinical adoption considerations for radiomics-based CAD. Particular emphasis is given to the enormous potential for enhancing diagnosis precision, treatment personalization, and overall patient outcomes.