Artificial Intelligence Prediction Model Research Articles

Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods

ABSTRACT Objectives To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models. Materials and Methods A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded. On each cephalogram, 78 landmarks were detected using PIPNet-based AI. Prediction models consisted of 132 predictor variables and 88 outcome variables. Predictor variables were demographics (age, sex), clinical (treatment time, premolar extraction), and Cartesian coordinates of the 64 anatomic landmarks. Outcome variables were Cartesian coordinates of the 22 soft tissue and 22 hard tissue landmarks after orthodontic treatment. The AI prediction model was based on the TabNet deep neural network. Two conventional statistical methods, multivariate multiple linear regression (MMLR) and partial least squares regression (PLSR), were each implemented for comparison. Prediction accuracy among the methods was compared. Results Overall, MMLR demonstrated the most accurate results, while AI was least accurate. AI showed superior predictions in only 5 of the 44 anatomic landmarks, all of which were soft tissue landmarks inferior to menton to the terminal point of the neck. Conclusions When predicting changes following orthodontic treatment, AI was not as effective as conventional statistical methods. However, AI had an outstanding advantage in predicting soft tissue landmarks with substantial variability. Overall, results may indicate the need for a hybrid prediction model that combines conventional and AI methods.

Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit

Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatment, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.

Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sepsis in the pediatric intensive care unit (PICU). This retrospective observational study was conducted in the PICUs of the First Affiliated Hospital of Sun Yat-sen University from December 2016 to June 2019 and Shenzhen Children's Hospital from January 2019 to July 2020. The children were divided into a death group and a survival group. Different machine language (ML) models were used to predict the risk of death in children with sepsis. A total of 671 children with sepsis were enrolled. The accuracy (ACC) of the artificial neural network model was better than that of support vector machine, logical regression analysis, Bayesian, K nearest neighbor method and decision tree models, with a training set ACC of 0.99 and a test set ACC of 0.96. The AI model can be used to predict the risk of death due to sepsis in children in the PICU, and the artificial neural network model is better than other AI models in predicting mortality risk.

Combining artificial neural networks and a marginal structural model to predict the progression from depression to Alzheimer's disease

IntroductionDecades of research in population health have established depression as a likely precursor to Alzheimer's disease. A combination of causal estimates and machine learning methods in artificial intelligence could identify internal and external mediating mechanisms that contribute to the likelihood of progression from depression to Alzheimer's disease.MethodsWe developed an integrated predictive model, combining the marginal structural model and an artificial intelligence predictive model, distinguishing between patients likely to progress from depressive states to Alzheimer's disease better than each model alone.ResultsThe integrated predictive model achieved substantial clinical relevance when using the area under the curve measure. It performed better than the traditional statistical method or a single artificial intelligence method alone.DiscussionThe integrated predictive model could form a part of a clinical screening tool that identifies patients who are likely to progress from depression to Alzheimer's disease for early behavioral health interventions. Given the high costs of treating Alzheimer's disease, our model could serve as a cost-effective intervention for the early detection of depression before it progresses to Alzheimer's disease.

A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm

We constructed an early prediction model for postoperative pulmonary complications after thoracoscopic surgery using machine learning and deep learning algorithms. The artificial intelligence prediction models were built in Python, primarily using artificial intelligencealgorithms including both machine learning and deep learning algorithms. Correlation analysis showed that postoperative pulmonary complications were positively correlated with age and surgery duration, and negatively correlated with serum albumin. Using the light gradient boosting machine(LGBM) algorithm, weighted feature engineering revealed that single lung ventilation duration, history of smoking, surgery duration, ASA score, and blood glucose were the main factors associated with postoperative pulmonary complications. Results of artificial intelligence algorithms for predicting pulmonary complications after thoracoscopy in the test group: In terms of accuracy, the two best algorithms were Logistic Regression (0.831) and light gradient boosting machine(0.827); in terms of precision, the two best algorithms were Gradient Boosting (0.75) and light gradient boosting machine (0.742); in terms of recall, the three best algorithms were gaussian naive bayes (0.581), Logistic Regression (0.532), and pruning Bayesian neural network (0.516); in terms of F1 score, the two best algorithms were LogisticRegression (0.589) and pruning Bayesian neural network (0.566); and in terms of Area Under Curve(AUC), the two best algorithms were light gradient boosting machine(0.873) and pruning Bayesian neural network (0.869). The results of this study suggest that pruning Bayesian neural network (PBNN) can be used to assess the possibility of pulmonary complications after thoracoscopy, and to identify high-risk groups prior to surgery.

Long time series of ocean wave prediction based on PatchTST model

In recent years, AI (artificial intelligence) algorithms based on big data have gradually emerged and been applied to short time predictions of ocean waves (e.g. 1 h, 3 h, …, 48 h). However, limited by the lack of a better method and time-consuming computation, long time (e.g. 720 h) predictions have not been investigated yet. Based on the problems with study of long time series wave prediction, by using the hourly wave reanalysis data near Bering Sea from 2016 to 2020, this study establishes a long time series AI prediction model for waves with a forecast length of one month (720 h) based on PatchTST model. The time series prediction results of significant wave height evolution in 2021 show that compared with Informer, LSTM (Long Short Term Memory) and NeuralProphet, PatchTST's is significantly superior with COR (Correlation) averages at 0.90, RMSE (Root Mean Square Error) averages at 0.65 and MAE (Mean Absolute Error) averages at 0.56 on 12 months of 2021, which can be effectively applied to long time significant wave height prediction.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction

Objective. In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of ‘signal-image-knowledge’ has remained unchanged. However, the process of ‘signal to image’ inevitably introduces information distortion, ultimately leading to irrecoverable biases in the ‘image to knowledge’ process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal). Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of ‘human-signal-image’ using the workflow ‘CT-simulated data- reconstructed CT,’ and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data. Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866). Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of ‘signal-to-image’ can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of ‘signal-image-knowledge’, opening up new avenues for more accurate medical diagnostics.

A study on nitrogen oxide emission prediction in Taichung thermal power plant using artificial intelligence (AI) model

In recent studies, artificial intelligence models have been developed for the prediction of nitrogen oxide emissions from thermal power plants. This research utilizes an artificial intelligence prediction model with more coal input features than boiler features, including volatility, ash content, sulfur content, fixed carbon, total moisture, calorific value, grinding rate, fuel ratio, coal feeding rate, boiler efficiency, total air volume, and excess air volume. The paper delves into the importance analysis of input features for artificial intelligence models. Moreover, feature importance analysis is not only a prerequisite for predicting nitrogen oxide emissions but also a study providing insights into model performance. An artificial neural networks (ANN) regression model is employed to predict nitrogen oxide emissions, and the results demonstrate that the number of feature importance significantly impacts model performance. The best model performance is achieved with eight specific input features. Finally, after the training and validation processes, the ANN model yields the optimal coefficient of determination (R2) value.

The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement

Abstract Aims Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Cardiac amyloidosis has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at-risk patients. Methods and results In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analysed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analyses using Cox regression were performed to compare clinical outcomes between patients with high CA probability vs. those with low probability at 1-year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had a clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG AI algorithm was significantly associated with increased all-cause mortality [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.01–1.96, P = 0.046] and higher rates of major adverse cardiovascular events (transient ischaemic attack (TIA)/stroke, myocardial infarction, and heart failure hospitalizations] (HR 1.36, 95% CI 1.01–1.82, P = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95% CI 1.13–2.20, P = 0.008) at 1-year follow-up. There were no significant differences in TIA/stroke or myocardial infarction. Conclusion Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.

Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population

Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid ultrasound dataset with significant diagnostic performance disparities across subgroups linked causally to sample size imbalances. To address this, we introduced the Quasi-Pareto Improvement (QPI) approach and a deep learning implementation (QP-Net) combining multi-task learning and domain adaptation to improve model performance among disadvantaged subgroups without compromising overall population performance. On the thyroid ultrasound dataset, our method significantly mitigated the area under curve (AUC) disparity for three less-prevalent subgroups by 0.213, 0.112, and 0.173 while maintaining the AUC for dominant subgroups; we also further confirmed the generalizability of our approach on two public datasets: the ISIC2019 skin disease dataset and the CheXpert chest radiograph dataset. Here we show the QPI approach to be widely applicable in promoting AI for equitable healthcare outcomes.

252 (PB-068) Poster - Breast cancer patients’ communication needs and wishes for an explainable Artificial Intelligence prediction model for lymphedema

252 (PB-068) Poster - Breast cancer patients’ communication needs and wishes for an explainable Artificial Intelligence prediction model for lymphedema

Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis.

To review and compare the accuracy of convolutional neural networks (CNN) for the diagnosis of meniscal tears in the current literature and analyze the decision-making processes utilized by these CNN algorithms. PubMed, MEDLINE, EMBASE, and Cochrane databases up to December 2022 were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. Risk of analysis was used for all identified articles. Predictive performance values, including sensitivity and specificity, were extracted for quantitative analysis. The meta-analysis was divided between AI prediction models identifying the presence of meniscus tears and the location of meniscus tears. Eleven articles were included in the final review, with a total of 13,467 patients and 57,551 images. Heterogeneity was statistically significantly large for the sensitivity of the tear identification analysis (I2 = 79%). A higher level of accuracy was observed in identifying the presence of a meniscal tear over locating tears in specific regions of the meniscus (AUC, 0.939 vs 0.905). Pooled sensitivity and specificity were 0.87 (95% confidence interval (CI) 0.80-0.91) and 0.89 (95% CI 0.83-0.93) for meniscus tear identification and 0.88 (95% CI 0.82-0.91) and 0.84 (95% CI 0.81-0.85) for locating the tears. AI prediction models achieved favorable performance in the diagnosis, but not location, of meniscus tears. Further studies on the clinical utilities of deep learning should include standardized reporting, external validation, and full reports of the predictive performances of these models, with a view to localizing tears more accurately. Meniscus tears are hard to diagnose in the knee magnetic resonance images. AI prediction models may play an important role in improving the diagnostic accuracy of clinicians and radiologists. • Artificial intelligence (AI) provides great potential in improving the diagnosis of meniscus tears. • The pooled diagnostic performance for artificial intelligence (AI) in identifying meniscus tears was better (sensitivity 87%, specificity 89%) than locating the tears (sensitivity 88%, specificity 84%). • AI is good at confirming the diagnosis of meniscus tears, but future work is required to guide the management of the disease.

Commentary: An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer.

Commentary: An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer.

Synchrotron radiation data-driven artificial intelligence approaches in materials discovery

Synchrotron radiation technology provides high-resolution and high-sensitivity information for many fields such as material science, life science, and energy research. Synchrotron radiation data-driven methods have significantly accelerated the development of materials discovery and analysis. However, synchrotron radiation data is complex and large, requiring artificial intelligence for analysis. Artificial intelligence can efficiently process complex high-dimensional data, automate the analysis process, discover hidden patterns and associations, and build predictive models. This review provides an overview of the application and development of combining synchrotron radiation data-driven methods with artificial intelligence in the field of materials discovery. The application of the method in science is still limited by the problems of large and complex synchrotron radiation data, valuable experimental machine time, and uninterpretable artificial intelligence models. To address these problems, this review correspondingly proposes solutions for synchrotron radiation artificial intelligence data banks, standardized experiment records systems, and interpretable artificial intelligence predictive models.

The Use of Artificial Intelligence in Third Molar Surgery Risk Assessment

Third molar removal complication rates can be as high as 30%. Risk assessment tools may lower these rates. Artificial intelligence (AI) driven prediction models are a promising approach to predict possible unfavourable outcomes and cone beam computed tomography imaging may play an important role. AI prediction models are showing excellent results in research settings. To be implemented in clinical practice they will need to overcome some robustness, security, liability, and practical issues. If they do, AI prediction models can be integrated in electronic patient record systems by alerting clinicians in case of an imminent unfavourable outcome so it can be avoided. CPD/Clinical Relevance: Artificial intelligence-driven risk assessment tools will lower complications in third molar surgery.

Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals

Green toxicology is marching chemistry into the 21st century. This emerging framework will transform how chemical safety is evaluated by incorporating evaluation of the hazards, exposures, and risks associated with chemicals into early product development in a way that minimizes adverse impacts on human and environmental health. The goal is to minimize toxic threats across entire supply chains through smarter designs and policies. Traditional animal testing methods are replaced by faster, cutting-edge innovations like organs-on-chips and artificial intelligence predictive models that are also more cost-effective. Core principles of green toxicology include utilizing alternative test methods, applying the precautionary principle, considering lifetime impacts, and emphasizing risk prevention over reaction. This paper provides an overview of these foundational concepts and describes current initiatives and future opportunities to advance the adoption of green toxicology approaches. Chal-lenges and limitations are also discussed. Green shoots are emerging with governments offering carrots like the European Green Deal to nudge industry. Noteworthy, animal rights and environ-mental groups have different ideas about the needs for testing and their consequences for animal use. Green toxicology represents the way forward to support both these societal needs with sufficient throughput and human relevance for hazard information and minimal animal suffering. Green toxi-cology thus sets the stage to synergize human health and ecological values. Overall, the integration of green chemistry and toxicology has potential to profoundly shift how chemical risks are evaluated and managed to achieve safety goals in a more ethical, ecologically-conscious manner.

Improvement of an Artificial Intelligence Algorithm Prediction Model Based on the Similarity Method: A Case Study of Office Building Cooling Load Prediction

Artificial intelligence algorithms (AIAs) have gained widespread adoption in air conditioning load prediction. However, their prediction accuracy is substantially influenced by the quality of training samples. To improve the prediction accuracy of air conditioning load, this study presents an AIA prediction model based on the method of similarity sample screening. Initially, the comprehensive similarity coefficient between samples was obtained by using the gray correlation method improved with information entropy. Subsequently, a subset of closely related samples was extracted from the original dataset and employed to train the artificial intelligence prediction model. Finally, the trained AIA prediction model was used to predict the air conditioning load. The results illustrate that the method of similarity sample screening effectively improved the prediction accuracy of BP neural network (BPNN) and extreme learning machine (ELM) prediction models. However, it is essential to note that this approach may not be suitable for genetic algorithm BPNN (GABPNN) and support vector regression (SVR) models.

Facial profile evaluation and prediction of skeletal class II patients during camouflage extraction treatment: a pilot study

BackgroundThe evaluation of the facial profile of skeletal Class II patients with camouflage treatment is of great importance for patients and orthodontists. The aim of this study is to explore the key factors in evaluating the facial profile esthetics and to predict the posttreatment facial profile esthetics of skeletal Class II extraction patients.Methods124 skeletal Class II extraction patients were included. The pretreatment and posttreatment cephalograms were analyzed by a trained expert orthodontist. The facial profile esthetics of pretreatment and posttreatment lateral photographs were evaluated by 10 expert orthodontists using the visual analog scale (VAS). The correlation between subjective facial profile esthetics and objective cephalometric measurements was assessed. Three machine-learning methods were used to predict posttreatment facial profile esthetics.ResultsThe distances from lower and upper lip to the E plane and U1-APo showed the stronger correlation with profile esthetics. The changes in lower lip to the E plane and U1-APo during extraction exhibited the stronger correlation with changes in VAS score (r = − 0.551 and r = − 0.469). The random forest prediction model had the lowest mean absolute error and root mean square error, demonstrating a better prediction accuracy and fitting effect. In this model, pretreatment upper lip to E plane, pretreatment Pog-NB and the change of U1-GAll were the most important variables in predicting the posttreatment score of facial profile esthetics.ConclusionsThe maxillary incisor protrusion and lower lip protrusion are key objective indicators for evaluating and predicting facial profile esthetics of skeletal Class II extraction patients. An artificial intelligence prediction model could be a new method for predicting the posttreatment esthetics of facial profiles.

Role of arachidonic acid metabolism in intervertebral disc degeneration: identification of potential biomarkers and therapeutic targets via multi-omics analysis and artificial intelligence strategies

BackgroundIntervertebral disc degeneration (IVDD) is widely recognized as the primary etiological factor underlying low back pain, often necessitating surgical intervention as the sole recourse in severe cases. The metabolic pathway of arachidonic acid (AA), a pivotal regulator of inflammatory responses, influences the development and progression of IVDD.MethodsInitially, a comparative analysis was conducted to investigate the relationship between AA expression patterns and different stages of IVDD using single-cell sequencing (scRNA-seq) data. Additionally, three machine learning methods (LASSO, random forest, and support vector machine recursive feature elimination) were employed to identify hub genes associated with IVDD. Subsequently, a novel artificial intelligence prediction model was developed for IVDD based on an artificial neural network algorithm and validated using an independent dataset. The identified hub genes were further subjected to functional enrichment, immune infiltration, and Connectivity Map analysis. Moreover, external validation was performed using flow cytometry and real-time reverse transcription polymerase chain reaction analysis.ResultsBoth scRNA-seq and bulk RNA-seq data revealed a positive correlation between the severity of IVDD and the AA metabolic pathway. They also revealed increased AA metabolic activity in macrophages and neutrophils, as well as enhanced intercellular communication with nucleus pulposus cells. Utilizing advanced machine learning algorithms, five hub genes (AKR1C3, ALOX5, CYP2B6, EPHX2, and PLB1) were identified, and an incipient diagnostic model was developed with an AUC of 0.961 in the training cohort and 0.72 in the validation cohort. An in-depth exploration of the functionality of these hub genes revealed their notable association with inflammatory responses and immune cell infiltration. Lastly, AH6809 was found to delay IVDD by inhibiting AKR1C3.ConclusionsThis study offers comprehensive insights into potential biomarkers and small molecules associated with the early pathogenesis of IVDD. The identified biomarkers and the developed integrated diagnostic model hold great promise in predicting the onset of early IVDD. AH6809 was established as a therapeutic target for AKR1C3 in the treatment of IVDD, as evidenced by computer simulations and biological experiments.

An Artificial Intelligence Prediction Model of Insulin Sensitivity, Insulin Resistance, and Diabetes Using Genes Obtained through Differential Expression.

Insulin is a powerful pleiotropic hormone that affects processes such as cell growth, energy expenditure, and carbohydrate, lipid, and protein metabolism. The molecular mechanisms by which insulin regulates muscle metabolism and the underlying defects that cause insulin resistance have not been fully elucidated. This study aimed to perform a microarray data analysis to find differentially expressed genes. The analysis has been based on the data of a study deposited in Gene Expression Omnibus (GEO) with the identifier "GSE22309". The selected data contain samples from three types of patients after taking insulin treatment: patients with diabetes (DB), patients with insulin sensitivity (IS), and patients with insulin resistance (IR). Through an analysis of omics data, 20 genes were found to be differentially expressed (DEG) between the three possible comparisons obtained (DB vs. IS, DB vs. IR, and IS vs. IR); these data sets have been used to develop predictive models through machine learning (ML) techniques to classify patients with respect to the three categories mentioned previously. All the ML techniques present an accuracy superior to 80%, reaching almost 90% when unifying IR and DB categories.