Automatic Artificial Intelligence Research Articles

Artificial Intelligence Quantification of Enhanced Synovium Throughout the Entire Hand in Rheumatoid Arthritis on Dynamic Contrast-Enhanced MRI.

Challenges persist in achieving automatic and efficient inflammation quantification using dynamic contrast-enhanced (DCE) MRI in rheumatoid arthritis (RA) patients. To investigate an automatic artificial intelligence (AI) approach and an optimized dynamic MRI protocol for quantifying disease activity in RA in whole hands while excluding arterial pixels. Retrospective. Twelve RA patients underwent DCE-MRI with 27 phases for creating the AI model and tested on images with a variable number of phases from 35 RA patients. 3.0 T/DCE T1-weighted gradient echo sequence (mDixon, water image). The model was trained with various DCE-MRI time-intensity number of phases. Evaluations were conducted for similarity between AI segmentation and manual outlining in 51 ROIs with synovitis. The relationship between synovial volume via AI segmentation with rheumatoid arthritis magnetic resonance imaging scoring (RAMRIS) across whole hands was then evaluated. The reference standard was determined by an experienced musculoskeletal radiologist. Area under the curve (AUC) of receiver operating characteristic (ROC), Dice and Spearman's rank correlation coefficients, and interclass correlation coefficient (ICC). A P-value <0.05 was considered statistically significant. A minimum of 15 phases (acquisition time at least 2.5 minutes) was found to be necessary. AUC ranged from 0.941 ± 0.009 to 0.965 ± 0.009. The Dice score was 0.557-0.615. Spearman's correlation coefficients between the AI model and ground truth were 0.884-0.927 and 0.736-0.831, for joint ROIs and whole hands, respectively. The Spearman's correlation coefficient for the additional test set between the model trained with 15 phases and RAMRIS was 0.768. The AI-based classification model effectively identified synovitis pixels while excluding arteries. The optimal performance was achieved with at least 15 phases, providing a quantitative assessment of inflammatory activity in RA while minimizing acquisition time. 3 TECHNICAL EFFICACY: Stage 2.

Revolutionizing Cybersecurity Audit through Artificial Intelligence Automation: A Comprehensive Exploration

Revolutionizing Cybersecurity Audit through Artificial Intelligence Automation: A Comprehensive Exploration

Ethical Considerations in AI Simulations for Designing Assistive Technologies

Current ethical debates on the use of artificial intelligence (AI) in healthcare approach AI technology in three primary ways. First, they assess the risks and potential benefits of current AI-enabled products using ethical checklists. Second, they propose ex ante lists of ethical values relevant to the design and development of assistive technologies. Third, they advocate for incorporating moral reasoning into AI's automation processes. These three perspectives dominate the discourse, as evidenced by a brief literature summary. We propose a fourth approach: viewing AI as a methodological tool to aid ethical reflection. This involves an AI simulation concept informed by three elements: 1) stochastic human behavior models based on behavioral data for simulating realistic scenarios, 2) qualitative empirical data on value statements regarding internal policy, and 3) visualization components to illustrate the impact of variable changes. This approach aims to inform an interdisciplinary field about anticipated ethical challenges or trade-offs in specific settings, prompting a re-evaluation of design and implementation plans. This is particularly useful for applications involving complex values and behaviors or limited communication resources, such as dementia care or care for individuals with cognitive impairments. While simulation does not replace ethical reflection, it allows for detailed, context-sensitive analysis during the design process and before implementation.Finally, we discuss the quantitative analysis methods enabled by stochastic simulations and the potential for these simulations to enhance traditional thought experiments and future-oriented technology assessments.

Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection.

Alzheimer's disease (AD) is a neurodegenerative illness that impairs cognition, function, and behavior by causing irreversible damage to multiple brain areas, including the hippocampus. The suffering of the patients and their family members will be lessened with an early diagnosis of AD. The automatic diagnosis technique is widely required due to the shortage of medical experts and eases the burden of medical staff. The automatic artificial intelligence (AI)-based computerized method can help experts achieve better diagnosis accuracy and precision rates. This study proposes a new automated framework for AD stage prediction based on the ResNet-Self architecture and Fuzzy Entropy-controlled Path-Finding Algorithm (FEcPFA). A data augmentation technique has been utilized to resolve the dataset imbalance issue. In the next step, we proposed a new deep-learning model based on the self-attention module. A ResNet-50 architecture is modified and connected with a self-attention block for important information extraction. The hyperparameters were optimized using Bayesian optimization (BO) and then utilized to train the model, which was subsequently employed for feature extraction. The self-attention extracted features were optimized using the proposed FEcPFA. The best features were selected using FEcPFA and passed to the machine learning classifiers for the final classification. The experimental process utilized a publicly available MRI dataset and achieved an improved accuracy of 99.9%. The results were compared with state-of-the-art (SOTA) techniques, demonstrating the improvement of the proposed framework in terms of accuracy and time efficiency.

Revolutionizing Healthcare with Smarter AI: In-depth Exploration of Advancements, Challenges, and Future Directions

Artificial intelligence (AI) is the main branch of computer science that permits advanced machines to interpret and analyze complex healthcare data elaborating the recent challenges in the medical field of study. The current state of AI applications in healthcare is examined in this systematic literature review, with an emphasis on the technology's accomplishments, difficulties, and potential. The wide breadth of AI technologies used in healthcare settings, such as robots, computer vision, machine learning, and natural language processing, is highlighted in this review through an extensive analysis of peer-reviewed publications. It talks about how customized medicine, predictive analytics, illness detection, and treatment planning are just a few of the areas of healthcare delivery that AI-driven technologies are transforming. According to research by investment bank Goldman Sachs, 300 million full-time employees could be replaced by artificial intelligence (AI). In the US and Europe, it might replace 25% of labor duties, but it might also lead to an increase in productivity and the creation of new jobs. Additionally, it might eventually result in a 7% rise in the global annual value of products and services produced. Additionally, the paper projects that approximately 25% of all employment might be performed totally by AI and that two-thirds of jobs in the U.S. and Europe "are exposed to some degree of AI automation. "The most likely groups to be impacted by workforce automation are educated white-collar workers making up to $80,000 annually, according to research from OpenAI and the University of Pennsylvania. According to a McKinsey Global Institute study, developments in digitalization, robots, and artificial intelligence may require at least 14% of workers worldwide to change jobs by 2030.

Artificial intelligence-assisted full-mouth radiograph mounting in dental education.

With the increasing prevalence of artificial intelligence (AI) and the significant research gap in the application of AI within dentistry, this study aimed to (1) evaluate the efficiency and accuracy of dental students in full-mouth radiograph series (FMS) mounting with and without AI assistance, and (2) assess dental students' perceptions of AI in clinical education to address the impact of AI in dental education. An AI-based interface for mounting radiographs on FMS templates was designed and implemented in the study. Forty third-year dental students were randomly assigned to control and test groups. The control group manually mounted FMS radiographs, while the test group reviewed AI-pre-mounted radiographs for adjustments. Students' performance in efficiency and accuracy was evaluated. Pre- and post-study surveys were conducted to gauge students' confidence levels and opinions regarding the usefulness of the AI-assisted program. The test group (using AI) demonstrated significantly faster radiograph mounting times than the control group (manual) (p<0.05). Accuracy was lower in the test groups, when comparing AI-assisted and manual mounting of FMS (p<0.01). Self-confidence and confidence in AI were consistent between the control and test groups, both before and after the study. Students with AI presented with a decreased accuracy in FMS radiograph mounting. Therefore, AI automation could potentially have negative impacts in a learning environment with inexperienced clinicians.

Deep Learning For Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study

PurposeTo develop and externally validate an automatic Artificial Intelligence (AI) tool for delineating gross tumor volume (GTV) in esophageal squamous cell carcinoma (ESCC) patients, which can assist in the neo-adjuvant or radical radiation therapy treatment planning. Methods and MaterialsIn this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by two experts via consensus were used as ground truth. A three-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in three validation cohorts. The AI tool was compared against twelve board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance (ASD). Additionally, our previously established radiomics model for predicting pathological complete response (pCR) was utilized to compare AI-generated and ground truth contours, in order to assess the potential of the AI contouring tool in radiomics analysis. ResultsThe AI tool demonstrated good GTV contouring performance in multi-center validation cohorts, with median DSC values of 0.865, 0.876, and 0.866, and median ASD values of 0.939 mm, 0.789 mm, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of twelve board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = 0.003-0.048), reduced the intra- and inter-observer variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pCR prediction performance for these contours (P = 0.430) were observed. ConclusionsOur AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicated its potential on GTV contouring during radiation therapy treatment planning and radiomics studies.

The Transformative Impact of AI on Financial Institutions, with a Focus on Banking

The financial landscape is undergoing a profound transformation, with Artificial Intelligence (AI) emerging as the catalyst for unprecedented change in banking institutions. This paper briefly examines the multifaceted impact of AI across critical domains, encompassing customer experiences, security protocols, risk management, operational efficiency, return on investment, and regulatory compliance. In customer-centric evolution, AI-driven chatbots and virtual assistants redefine interactions, providing personalized and anticipatory services. In the field of finance, security is a paramount concern. At the same time, we are witnessing AI as the guardian, fortifying trust through real-time fraud detection and biometric authentication. Risk management undergoes a paradigm shift, with predictive analytics steering financial institutions toward strategic advantages in navigating dynamic markets. Operational excellence is achieved through AI's automation, liberating human resources for strategic endeavors, and fostering a costconscious ethos. Regulatory compliance, often intricate, finds harmony through AI-driven automation tools and biometric authentication, ensuring transparency and accountability in the face of relentless regulatory scrutiny. Striking the right balance between innovation and ethical considerations is pivotal in ensuring that the consolidation of AI and finance is a force for good.

Does artificial intelligence kill employment growth: the missing link of corporate AI posture.

An intense debate has been on-going about how artificial intelligence (AI) technology investments have an impact on employment. The debate has often focused on the potential of AI for human task automation, omitting the strategic incentive for firms to cooperate with their workers as to exploit AI technologies for the most relevant benefit of new product and service innovation. We calibrate an empirical probit regression model of how changes in employment relate to AI diffusion, based on formalizing a game-theoretical model of a firm exploiting the twin role of AI innovation and AI automation for both absolute and competitive advantage. The theoretical game-theory prediction is that employment following AI technology adoption is not negative, and ultimately depends on how AI leads to new success in innovation, competition which defines the competitive reward of innovation and profit sharing between workers and firms. Our estimation, is based on a global survey of 3,000 large companies across 10 countries, demonstrates that a firm employment growth depends on two strategic postures, that is, the firm relative maturity of AI adoption as well as its relative bias toward AI-based product innovation. The contribution of this research is to highlight the twin role of firm and workers in shaping how technology will affect employment. AI in particular marries the potential of task automation with even more potential for expansion.

Artificial Intelligence-based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed Patients.

Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests. Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient ( r ) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO). We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82, r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC ( P = 0.001) and FVC ( P = 0.04) values for the higher PPV patients, but not for DLCO ( P = 0.19). We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC.

A Fully Automatic Estimation of Tear Meniscus Height Using Artificial Intelligence.

Accurate quantification measurement of tear meniscus is vital for the precise diagnosis of dry eye. In current clinical practice, the measurement of tear meniscus height (TMH) relies on doctors' manual operation. This study aims to propose a novel automatic artificial intelligence (AI) system to evaluate TMH. A total of 510 photographs obtained by the oculus camera were labeled. Three thousand and five hundred images were finally attained by data enhancement to train the neural network model parameters, and 60 were used to evaluate the model performance in segmenting the cornea and tear meniscus region. One hundred images were used to test generalization ability of the model. We modified a segmentation model of the cornea and the tear meniscus based on the UNet-like network. The output of the segmentation model is followed by a calculation module that calculates and reports the TMH. Compared with ground truth (GT) manually labeled by clinicians, our modified model achieved a Dice Similarity Coefficient (DSC) and Intersection over union (Iou) of 0.99/0.98 in the corneal segmentation task and 0.92/0.86 for the detection of tear meniscus on the validation set, respectively. On the test set, the TMH automatically measured by our AI system strongly correlates with the results manually calculated by the ophthalmologists. We developed a fully automated and reliable AI system to obtain TMH. After large-scale clinical testing, our method could be used for dry eye screening in clinical practice.

The impact of artificial intelligence on modern education: prospects and challenges

This paper examines the intricate links between artificial intelligence (AI) and education, delving into both theoretical and practical aspects while evaluating possible ramifications for labor market dynamics, professional activity, and wider educational paradigms. Our research methodology involved analyzing relevant scientific literature, classifying data, consulting with subject matter experts, and synthesizing the results. Our research suggests that AI has the ability to greatly improve pedagogical processes, personalize learning experiences to meet individual student needs, and successfully address the time and financial limitations that are inherent in traditional educational models. However, our study also reveals challenges related to data confidentiality, potential plagiarism and fraud associated with AI use, and socioeconomic disparities resulting from unequal technology access. Additionally, we identified a significant gap in current AI usage standards legislation. It is essential for researchers, educators, and policymakers to recognize the potential risks of AI implementation in educational settings and proactively develop strategies that prioritize ethics, safety, and effectiveness. With labor market trends favoring specialists knowledgeable in utilizing AI tools, a consequent change in curricula is expected. In response to our findings, we recommend the creation of new academic disciplines that concentrate on the cultivation of AI expertise; the establishment of comprehensive national AI strategies; the crafting of retraining roadmaps for those who may be affected by AI automation; the inclusion of online AI courses in existing educational programs; and the promotion of grant funding for future AI research. Our future research will concentrate on reducing the potential negative impacts of integrating AI into educational systems.

Externally validated deep learning model to identify prodromal Parkinson’s disease from electrocardiogram

Little is known about electrocardiogram (ECG) markers of Parkinson’s disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case–control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Clinical data were available from May, 2014 onward at LUC and from January, 2015 onward at MLH, while the ECG data were available as early as 1990 in both institutes. PD was denoted by at least two primary diagnostic codes (ICD9 332.0; ICD10 G20) at least 30 days apart. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. ECGs obtained at least 6 months before PD incidence date were modeled to predict a subsequent diagnosis of PD within three time windows: 6 months–1 year, 6 months–3 years, and 6 months–5 years. We applied a novel deep neural network using standard 10-s 12-lead ECGs to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for sex, race and age were also performed. Our primary prediction model was a one-dimensional convolutional neural network (1D-CNN) that was built using 131 cases and 1058 controls from MLH, and externally validated on 29 cases and 165 controls from LUC. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation AUC of 0.67 when predicting future PD at any time between 6 months and 5 years after the ECG. Accuracy increased when restricted to ECGs obtained within 6 months to 3 years before PD diagnosis (AUC 0.69) and was highest when predicting future PD within 6 months to 1 year (AUC 0.74). The 1D-CNN model based on raw ECG data outperformed multiple models built using more standard ECG feature engineering approaches. These results demonstrate that a predictive model developed in one cohort using only raw 10-s ECGs can effectively classify individuals with prodromal PD in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help identify individuals with prodromal PD for cost-effective population-level early detection and inclusion in disease-modifying therapeutic trials.

Optimizing Resource Allocation in Cloud Infrastructure through AI Automation: A Comparative Study

Optimizing resource allocation in cloud infrastructure is paramount for ensuring efficient utilization of computing resources and minimizing operational costs. With the proliferation of diverse workloads and dynamic user demands, manual resource management becomes increasingly challenging. In this context, artificial intelligence (AI) automation emerges as a promising approach to enhance resource allocation efficiency. This paper presents a comparative study of various AI techniques applied to optimize resource allocation in cloud environments. We explore the efficacy of machine learning, evolutionary algorithms, and deep reinforcement learning methods in dynamically allocating resources to meet performance objectives while minimizing costs. Through a comprehensive evaluation of these approaches using real-world datasets and simulation experiments, we highlight their strengths, limitations, and comparative performance. Our findings provide valuable insights into the effectiveness of AI-driven resource allocation strategies, enabling cloud providers and practitioners to make informed decisions for enhancing cloud infrastructure management

Objective and automatic grading system of facial signs from smartphones' pictures in South African men: Validation versus dermatologists and characterization of changes with age.

To evaluate the capacity of the automatic detection system to accurately grade, from selfie pictures, the severity of eight facial signs in South African men. Selfie pictures (obtained from frontal and back cameras) of 281 South African men differently aged (20-70years) were obtained and analyzed by an automatic artificial intelligence (AI)-based automatic grading system. Data were compared with the clinical gradings made by experts and dermatologists. In all facial signs, both series of gradings were found highly correlated with, however, different coefficients (0.59-0.95), those of marionette lines and cheek pores being of lower values. No differences were observed between data obtained by frontal and back cameras. With age, in most cases, gradings show up to the 50-59year age-class, linear-like changes. When compared to men of other ancestries, South African men present lower wrinkles/texture, pigmentation, and ptosis/sagging scores till 50-59years, albeit not much different in the cheek pores sign. The early onset (mean age) of visibility of wrinkles/texture for South African men were (i.e., reaching grade >1) 39 and 45years for ptosis/sagging. This study completes and enlarges the previous works conducted on men of other ancestries by showing some South African specificities and slight differences with men of comparable phototypes (Afro American).

A Machine-Learning Approach for Automatic Grape-Bunch Detection Based on Opponent Colors

This paper presents a novel and automatic artificial-intelligence (AI) method for grape-bunch detection from RGB images. It mainly consists of a cascade of support vector machine (SVM)-based classifiers that rely on visual contrast-based features that, in turn, are defined according to grape bunch color visual perception. Due to some principles of opponent color theory and proper visual contrast measures, a precise estimate of grape bunches is achieved. Extensive experimental results show that the proposed method is able to accurately segment grapes even in uncontrolled acquisition conditions and with limited computational load. Finally, such an approach requires a very small number of training samples, making it appropriate for onsite and real-time applications that are implementable on smart devices, usable and even set up by winemakers.

DeepHeartCT: A fully automatic artificial intelligence hybrid framework based on convolutional neural network and multi-atlas segmentation for multi-structure cardiac computed tomography angiography image segmentation.

Cardiac computed tomography angiography (CTA) is an emerging imaging modality for assessing coronary artery as well as various cardiovascular structures. Recently, deep learning (DL) methods have been successfully applied to many applications of medical image analysis including cardiac CTA structure segmentation. However, DL requires a large amounts of data and high-quality labels for training which can be burdensome to obtain due to its labor-intensive nature. In this study, we aim to develop a fully automatic artificial intelligence (AI) system, named DeepHeartCT, for accurate and rapid cardiac CTA segmentation based on DL. The proposed system was trained using a large clinical dataset with computer-generated labels to segment various cardiovascular structures including left and right ventricles (LV, RV), left and right atria (LA, RA), and LV myocardium (LVM). This new system was trained directly using high-quality computer labels generated from our previously developed multi-atlas based AI system. In addition, a reverse ranking strategy was proposed to assess the segmentation quality in the absence of manual reference labels. This strategy allowed the new framework to assemble optimal computer-generated labels from a large dataset for effective training of a deep convolutional neural network (CNN). A large clinical cardiac CTA studies (n = 1,064) were used to train and validate our framework. The trained model was then tested on another independent dataset with manual labels (n = 60). The Dice score, Hausdorff distance and mean surface distance were used to quantify the segmentation accuracy. The proposed DeepHeartCT framework yields a high median Dice score of 0.90 [interquartile range (IQR), 0.90-0.91], a low median Hausdorff distance of 7 mm (IQR, 4-15 mm) and a low mean surface distance of 0.80 mm (IQR, 0.57-1.29 mm) across all segmented structures. An additional experiment was conducted to evaluate the proposed DL-based AI framework trained with a small vs. large dataset. The results show our framework also performed well when trained on a small optimal training dataset (n = 110) with a significantly reduced training time. These results demonstrated that the proposed DeepHeartCT framework provides accurate and rapid cardiac CTA segmentation that can be readily generalized for handling large-scale medical imaging applications.

Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments

BackgroundCurrent radiological assessments of 18fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging data in diffuse large B-cell lymphoma (DLBCL) can be time consuming, do not yield real-time information regarding disease burden and organ involvement, and hinder the use of FDG-PET to potentially limit the reliance on invasive procedures (e.g. bone marrow biopsy) for risk assessment.MethodsOur aim is to enable real-time assessment of imaging-based risk factors at a large scale and we propose a fully automatic artificial intelligence (AI)-based tool to rapidly extract FDG-PET imaging metrics in DLBCL. On availability of a scan, in combination with clinical data, our approach generates clinically informative risk scores with minimal resource requirements. Overall, 1268 patients with previously untreated DLBCL from the phase III GOYA trial (NCT01287741) were included in the analysis (training: n = 846; hold-out: n = 422).ResultsOur AI-based model comprising imaging and clinical variables yielded a tangible prognostic improvement compared to clinical models without imaging metrics. We observed a risk increase for progression-free survival (PFS) with hazard ratios [HR] of 1.87 (95% CI: 1.31–2.67) vs 1.38 (95% CI: 0.98–1.96) (C-index: 0.59 vs 0.55), and a risk increase for overall survival (OS) (HR: 2.16 (95% CI: 1.37–3.40) vs 1.40 (95% CI: 0.90–2.17); C-index: 0.59 vs 0.55). The combined model defined a high-risk population with 35% and 42% increased odds of a 4-year PFS and OS event, respectively, versus the International Prognostic Index components alone. The method also identified a subpopulation with a 2-year Central Nervous System (CNS)-relapse probability of 17.1%.ConclusionOur tool enables an enhanced risk stratification compared with IPI, and the results indicate that imaging can be used to improve the prediction of central nervous system relapse in DLBCL. These findings support integration of clinically informative AI-generated imaging metrics into clinical workflows to improve identification of high-risk DLBCL patients.Trial RegistrationRegistered clinicaltrials.gov number: NCT01287741.Graphical

Development of deep learning segmentation models for coronary X-ray angiography: Quality assessment by a new global segmentation score and comparison with human performance

Introduction and objectivesAlthough automatic artificial intelligence (AI) coronary angiography (CAG) segmentation is arguably the first step toward future clinical application, it is underexplored. We aimed to (1) develop AI models for CAG segmentation and (2) assess the results using similarity scores and a set of criteria defined by expert physicians. MethodsPatients undergoing CAG were randomly selected in a retrospective study at a single center. Per incidence, an ideal frame was segmented, forming a baseline human dataset (BH), used for training a baseline AI model (BAI). Enhanced human segmentation (EH) was created by combining the best of both. An enhanced AI model (EAI) was trained using the EH. Results were assessed by experts using 11 weighted criteria, combined into a Global Segmentation Score (GSS: 0–100 points). Generalized Dice Score (GDS) and Dice Similarity Coefficient (DSC) were also used for AI models assessment. Results1664 processed images were generated. GSS for BH, EH, BAI and EAI were 96.9+/-5.7; 98.9+/-3.1; 86.1+/-10.1 and 90+/-7.6, respectively (95% confidence interval, p<0.001 for both paired and global differences). The GDS for the BAI and EAI was 0.9234±0.0361 and 0.9348±0.0284, respectively. The DSC for the coronary tree was 0.8904±0.0464 and 0.9134±0.0410 for the BAI and EAI, respectively. The EAI outperformed the BAI in all coronary segmentation tasks, but performed less well in some catheter segmentation tasks. ConclusionsWe successfully developed AI models capable of CAG segmentation, with good performance as assessed by all scores.

AI framework with computational box counting and Integer programming removes quantization error in fractal dimension analysis of optical images

Because many Chemical Engineering phenomena and processes involve distinctive shapes and structures, fractal dimension (FD) analysis is of great interest to the Chemical Engineering community due to its utility in providing a statistical index of shape complexity, which not only quantifies image structure but also helps explain functional properties. Past box counting (BC) methods for estimation of FDs are inconsistent due to quantization error (QE) introduced from image rotation and translation. In this work, we propose a systematic and automatic artificial intelligence (AI) framework that consistently estimates FDs of relevance to different fields of Chemical Engineering without QE by integrating image preprocessing, set-covering optimization-based BC, and regression analysis. As a result of the deterministic optimization technique, FD estimations remain consistent for all images regardless of image rotation and translation. The results of image datasets obtained from fields such as interfacial science, biomedical engineering, human anatomy, among others, demonstrate efficient box size specification to maximize regression effectiveness and FD calculation accuracy for a variety of images. Overall, the proposed AI framework offers a new means of estimating FD accurately and efficiently for optical images of interest to the Chemical Engineering community.