Int J Cardiovasc Imaging. 2025 Jan 9. doi: 10.1007/s10554-025-03322-z. Online ahead of print.

ABSTRACT

Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessment and better patient outcomes. The evolution of artificial intelligence (AI) presents new avenues for predicting heart failure mortality. Yet current research has predominantly leveraged structured data and unstructured clinical notes from electronic health records (EHR), underutilizing the prognostic value of chest X-rays (CXRs). This study aims to harness deep learning methodologies to explore the feasibility of enhancing the precision of predicting in-hospital all-cause mortality in heart failure patients using CXRs data. We propose a novel multimodal deep learning network based on the spatially and temporally decoupled Transformer (MN-STDT) for in-hospital mortality prediction in heart failure by integrating longitudinal CXRs and structured EHR data. The MN-STDT captures spatial and temporal information from CXRs through a Hybrid Spatial Encoder and a Distance-Aware Temporal Encoder, ultimately fusing features from both modalities for predictive modeling. Initial pre-training of the spatial encoder was conducted on CheXpert, followed by full model training and evaluation on the MIMIC-IV and MIMIC-CXR datasets for mortality prediction tasks. In a comprehensive view, the MN-STDT demonstrated the best performance, with an AUC-ROC of 0.8620, surpassing all baseline models. Comparative analysis revealed that the AUC-ROC of the multimodal model (0.8620) was significantly higher than that of models using only structured data (0.8166) or chest X-ray data alone (0.7479). This study demonstrates the value of CXRs in the prognosis of heart failure, showing that the combination of longitudinal CXRs with structured EHR data can significantly improve the accuracy of mortality prediction in heart failure. Feature importance analysis based on SHAP provides interpretable decision support, paving the way for potential clinical applications.

PMID:39786626 | DOI:10.1007/s10554-025-03322-z

Breast Cancer Res Treat. 2025 Jan 9. doi: 10.1007/s10549-024-07589-z. Online ahead of print.

ABSTRACT

PURPOSE: Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to compare the performance of a traditional machine learning CADe algorithm for synthetic 2D mammography to a deep learning-based AI algorithm for DBT on the same mammograms.

METHODS: Mammographic examinations from 764 patients (mean age 58 years ± 11) with 106 biopsy-proven cancers and 658 cancer-negative cases were analyzed by a CADe algorithm (ImageChecker v10.0, Hologic, Inc.) and an AI algorithm (Genius AI Detection v2.0, Hologic, Inc.). Synthetic 2D images were used for CADe analysis, and DBT images were used for AI analysis. For each algorithm, an overall case score was defined as the highest score of all lesion marks, which was used to determine the area under the receiver operating characteristic curve (AUC).

RESULTS: The overall AUC was higher for 3D AI than 2D CADe (0.873 versus 0.693, P < 0.001). Lesion-specific sensitivity of 3D AI was higher than 2D CADe (94.3 versus 72.6%, P = 0.002). Specificity of 3D AI was higher than 2D CADe (54.3 versus 16.7%, P < 0.001), and the rate of false marks on non-cancer cases was lower for 3D AI than 2D CADe (0.91 versus 3.24 per exam, P < 0.001).

CONCLUSION: A deep learning-based AI algorithm applied to DBT images significantly outperformed a traditional machine learning CADe algorithm applied to synthetic 2D mammographic images, with regard to AUC, sensitivity, and specificity.

PMID:39786500 | DOI:10.1007/s10549-024-07589-z

Drug Saf. 2025 Jan 9. doi: 10.1007/s40264-024-01505-6. Online ahead of print.

ABSTRACT

BACKGROUND: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear.

OBJECTIVE: To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources.

METHODS: A scoping review was conducted by searching six databases in July 2023. Studies leveraging NLP/ML to identify ADEs from EHR were included. Titles/abstracts were screened by two independent researchers as were full-text articles. Data extraction was conducted by one researcher and checked by another. A narrative synthesis summarises the research techniques, ADEs analysed, model performance and pharmacovigilance impacts.

RESULTS: Seven studies met the inclusion criteria covering a wide range of ADEs and medications. The utilisation of rule-based NLP, statistical models, and deep learning approaches was observed. Natural language processing/ML techniques with unstructured data improved the detection of under-reported adverse events and safety signals. However, substantial variability was noted in the techniques and evaluation methods employed across the different studies and limitations exist in integrating the findings into practice.

CONCLUSIONS: Natural language processing (NLP) and machine learning (ML) have promising possibilities in extracting valuable insights with regard to pharmacovigilance from unstructured EHR data. These approaches have demonstrated proficiency in identifying specific adverse events and uncovering previously unknown safety signals that would not have been apparent through structured data alone. Nevertheless, challenges such as the absence of standardised methodologies and validation criteria obstruct the widespread adoption of NLP/ML for pharmacovigilance leveraging of unstructured EHR data.

PMID:39786481 | DOI:10.1007/s40264-024-01505-6

J Chem Inf Model. 2025 Jan 9. doi: 10.1021/acs.jcim.4c01732. Online ahead of print.

ABSTRACT

Proteolysis-targeting chimeras (PROTACs) are heterobifunctional molecules that target undruggable proteins, enhance selectivity and prevent target accumulation through catalytic activity. The unique structure of PROTACs presents challenges in structural identification and drug design. Liquid chromatography (LC), combined with mass spectrometry (MS), enhances compound annotation by providing essential retention time (RT) data, especially when MS alone is insufficient. However, predicting RT for PROTACs remains challenging. To address this, we compiled the PROTAC-RT data set from literature and evaluated the performance of four machine learning algorithms─extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN) and support vector machines (SVM)─and a deep learning model, fully connected neural network (FCNN), using 24 molecular fingerprints and descriptors. Through screening combinations of molecular fingerprints, descriptors and chromatographic condition descriptors (CCs), we developed an optimized XGBoost model (XGBoost + moe206+Path + Charge + CCs) that achieved an R2 of 0.958 ± 0.027 and an RMSE of 0.934 ± 0.412. After hyperparameter tuning, the model's R2 improved to 0.963 ± 0.023, with an RMSE of 0.896 ± 0.374. The model showed strong predictive accuracy under new chromatographic separation conditions and was validated using six experimentally determined compounds. SHapley Additive exPlanations (SHAP) not only highlights the advantages of XGBoost but also emphasizes the importance of CCs and molecular features, such as bond variability, van der Waals surface area, and atomic charge states. The optimized XGBoost model combines moe206, path, charge descriptors, and CCs, providing a fast and precise method for predicting the RT of PROTACs compounds, thus facilitating their annotation.

PMID:39786356 | DOI:10.1021/acs.jcim.4c01732

Alzheimers Dement. 2024 Dec;20 Suppl 2:e087666. doi: 10.1002/alz.087666.

ABSTRACT

BACKGROUND: Drugs targeting Alzheimer's disease (AD) pathology are likely to be most effective in the presymptomatic stage, where individuals harbor AD pathology but have not manifested symptoms. Neuroimaging approaches can help to identify such individuals, but are costly for population-wide screening. Cost-effective screening is needed to identify those who may benefit from neuroimaging, such as those at risk of developing clinical disease. We present a deep learning algorithm that uses accelerometry recordings to predict clinically diagnosed AD in dementia-free patients.

METHOD: Participants were from The Memory and Aging Project (MAP), a longitudinal cohort study of older adults focused on aging and dementia. As part of this study, participants were asked to wear a wrist accelerometer for ten days. We designed a feedforward neural network that synthesizes clinical and accelerometric features to predict clinical AD. Clinical features were selected based on ease of collection, and included age, sex, education, social isolation and purpose in life. The dataset included participants without dementia at the time of recording, who survived for and had a known clinical AD outcome within five years of the recording.

RESULT: The training dataset consisted of 875 unique patients. The test dataset consisted of 395 unique patients (mean age 81.5, SD= 6.8). 14.4% of the test set developed clinical AD within five years. On the test set, the model achieved 89% sensitivity (SN), 70% specificity (SP), and an F1 score of 0.87 (Figure 1). This is superior to accelerometric-only (SN 67%, SP 70%, F1 0.68) and clinical-only (SN 76%, SP 80%, F1 0.78) models. Model accuracy was similar for patients both with and without mild cognitive impairment at baseline. When the binarized model output was used as a predictor for five-year AD-free survival via Cox proportional hazards (Figure 2), it achieved a C-index of 0.729 [95% CI 0.69 b-0.77], a hazard ratio of 6.90 [95% CI 4.15-11.47], and a log-likelihood ratio of 71.05. Further tuning and validation of the model are underway.

CONCLUSION: A deep learning model using wrist accelerometry and easily obtained clinical features shows promise in predicting 5-year AD outcomes in adults without dementia at baseline.

PMID:39786306 | DOI:10.1002/alz.087666

Alzheimers Dement. 2024 Dec;20 Suppl 2:e086725. doi: 10.1002/alz.086725.

ABSTRACT

BACKGROUND: The location of proposed brain MRI markers of small vessel disease (SVD) might reflect their pathogenesis and may translate into differential associations with cognition. We derived regional MRI markers of SVD and studied: (i) associations with cognitive performance, (ii) patterns most likely to reflect underlying SVD, (iii) mediating effects on the relationships of age and cardiovascular disease (CVD) risk with cognition.

METHOD: In 891 participants from The Multi-Ethnic Study of Atherosclerosis, we segmented enlarged perivascular spaces (ePVS), white matter hyperintensities (WMH) and microbleeds (MBs) using deep learning-based algorithms, and calculated white matter (WM) microstructural integrity measures of fractional anisotropy (FA), trace (TR) and free water (FW) using automated DTI-processing pipelines. Measures of global and domain-specific cognitive performance were derived from a comprehensive cognitive evaluation based on the UDS v3 neuropsychological battery.

RESULT: Mean (SD) age was 73.6 (7.9) years; 474 (53%) participants were women. In generalized linear models adjusted for demographics, vascular risk factors, and APOE ε4 carriership, higher basal ganglia ePVS count was associated with worse global, language, and attention cognitive performance (Table 1). Higher periventricular WMH volume was associated with worse global, delayed memory, language, phonemic, and attention performance. Higher WM FA was associated with better global, delayed memory, language, and attention performance. Higher WM TR was associated with worse global, delayed memory, language, phonemic, and attention performance. Exploratory factor analysis revealed that basal ganglia ePVS (standardized loading=0.51), thalamus ePVS (0.43), periventricular WMH (0.85), subcortical WMH (0.65), and WM FA (-0.73) and TR (0.84) loaded onto the same factor, likely reflecting underlying SVD. Structural equation models demonstrated that SVD mediated the effect of age on cognition (β[95%CI]= -0.071[-0.088,-0.053]) through the pathways: Age→SVD→Cognition (-0.044[-0.063,-0.026]) and Age→SVD→Brain Atrophy→Cognition (-0.006[-0.012,-0.002]) - Figure 1, and the effect of CVD risk on cognition (-0.028[-0.044,-0.012]) through the pathways: CVD Risk→SVD→Cognition (-0.021[-0.031,-0.013]) and CVD Risk→SVD→Brain Atrophy→Cognition (-0.007[-0.012,-0.003]) - Figure 2.

CONCLUSION: The location of the proposed MRI markers of SVD likely reflects distinct etiopathogenic substrates and should be considered when examining associations with cognitive or other health-related outcomes. SVD mediates the relationships of age and CVD risk with cognition via both atrophy-related and unrelated pathways.

PMID:39786253 | DOI:10.1002/alz.086725

Alzheimers Dement. 2024 Dec;20 Suppl 2:e090583. doi: 10.1002/alz.090583.

ABSTRACT

BACKGROUND: Neurodegenerative diseases are a heterogeneous group of illnesses. Differences across patients exist in the underlying biological drivers of disease. Furthermore, cross-diagnostic disease mechanisms exist, and different pathologies often co-occur in the brain. Clinical symptoms fail to capture this heterogeneity. Molecular biomarker-driven approaches are needed to improve patient identification & stratification with the aim of moving towards more targeted patient treatment strategies.

METHOD: The UK Biobank Pharma Proteomics Project has generated a proteomics dataset of unprecedented size. It consists of plasma proteomic profiles measured using the Olink 3k protein panel from over 54,000 individuals, complemented with broad phenotypic & genetic information. It is a unique resource to explore the biological correlation between neurodegenerative diseases as well as with other indications. We applied a multi-task deep learning (MTL) approach to generate models that predict disease status based on plasma proteomics profiles for a broad range of indications, including neurodegenerative diseases such as Alzheimer's and Parkinson's disease. The MTL approach first learns fundamental biological patterns by knowledge sharing between diseases in the initial segment of the network. Subsequently, understanding of a specific indication is refined with dedicated training. This improves model generalizability and statistical power. The neural network also produces low-dimensional embeddings of proteomic profiles that can be used for sample clustering and to derive insights about disease-associated processes.

RESULT: The average precision-recall area-under-the-curve (PR-AUC) of the MTL models across all diseases is 0.72 vs. 0.67 for the baseline single task logistic regression models. For Alzheimer's disease, the MTL classifier has a PR-AUC of 0.76. Feature importance scores were calculated using the SHAP method. Top features for Alzheimer's disease included several known biomarkers (e.g., GFAP, NPTXR). A UMAP projection of all diseases using the feature importance scores clusters diseases by disease category. Sample clustering revealed biologically interpretable patient subgroups, such as a Parkinson's cluster linked to lysosomal biology.

CONCLUSION: High performance of the MTL approach signifies good characterization of cross-disease biology. This is corroborated by the model's capability to produce meaningful low-dimensional representations of plasma proteomics profiles that can be used for identification of cross-diagnostic protein signatures and subtypes of neurodegenerative diseases. UKB application number 65851.

PMID:39786064 | DOI:10.1002/alz.090583

Alzheimers Dement. 2024 Dec;20 Suppl 6:e086486. doi: 10.1002/alz.086486.

ABSTRACT

BACKGROUND: Pivotal Alzheimer's Disease (AD) trials typically require thousands of participants, resulting in long enrollment timelines and substantial costs. We leverage deep learning predictive models to create prognostic scores (forecasted control outcome) of trial participants and in combination with a linear statistical model to increase statistical power in randomized clinical trials (RCT). This is a straightforward extension of the traditional RCT analysis, allowing for ease of use in any clinical program. We demonstrate the application of these methods retrospectively on 3 pivotal Phase III clinical trials in mild-to-moderate AD (NCT00236431, NCT00574132, and NCT00575055).

METHOD: A probabilistic deep learning model was trained on the trajectories of nearly 7000 participants who had varying degrees of cognitive impairment, ranging from mild cognitive impairment (MCI) to moderate AD. These trajectories were collected observational studies and the control arms of RCTs. This trained model was used to forecast the control outcomes of participants in the three trials retrospectively, by entering their individual trial baseline data. The resultant forecasts are known as prognostic scores and represent comprehensive predictions across a broad range of AD outcomes. We evaluated the potential reduction in estimated variance and how this could translate to required sample size by incorporating the prognostic score as a covariate in the primary linear statistical model of each study, analyzing the 11-component Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog11) and the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) endpoints as applicable.

RESULT: Prognostic scores have the potential to decrease estimated variance between 5% to 10% and placebo arm sample size between 7% and 17% in the 3 studies when comparing standard + prognostic score vs. standard adjustment.

CONCLUSION: Prognostic scores have the potential to increase the statistical power in clinical trials; this would enable a reduced number of subjects required to detect a significant treatment effect. Potential sample size reduction during trial planning must be carefully estimated using independent validation studies to reduce the risk of under-powering the trial.

PMID:39782540 | DOI:10.1002/alz.086486

Alzheimers Dement. 2024 Dec;20 Suppl 4:e087844. doi: 10.1002/alz.087844.

ABSTRACT

BACKGROUND: Clinical Dementia Rating (CDR) and its evaluation have been important nowadays as its prevalence in older ages after 60 years. Early identification of dementia can help the world to take preventive measures as most of them are treatable. The cellular Automata (CA) framework is a powerful tool in analyzing brain dynamics and modeling the prognosis of Alzheimer's disease.

METHOD: The proposed algorithm uses the CA framework to construct features for the classifier for the classification of classes in the dataset. A subject is assigned to a CA cell grid based on its feature values as rows and the specified number of cells in each row. When a CA grid receives a feature from a subject, the feature values are distributed among the cells using a transfer function. The distribution of featured values in the CA grid from the initialized cell to the neighboring cells in the row with a diffusion rate of 20%. Hence, redistributed CA images have been obtained for all the subjects. Deep learning architecture constituted with 4 layered Conv2d has been modeled for the classification of the CA images to classify low, moderate, and severe cognitive impairment.

RESULT: CDR from the ADNI dataset comprising 1948 subjects has been preprocessed for the six features and three classes (i.e., Low, moderate, and severe cognitive impairment) with 70% train sets and 30% test sets. A balanced dataset of 89 subjects for moderate and severe cognitive impairment has given the classification accuracy of 96%. A balanced dataset of 363 subjects for low and moderate cognitive impairment has given the classification accuracy of 95%.

CONCLUSION: A CA framework for the classification of cognitive impairment has been achieved with good accuracy. The implementation of the CA approach and its runtime performance has an advantage over the well-known algorithms by giving a good pathway in contributing to the classification problems.

PMID:39782453 | DOI:10.1002/alz.087844

Alzheimers Dement. 2024 Dec;20 Suppl 4:e083965. doi: 10.1002/alz.083965.

ABSTRACT

BACKGROUND: With the advent of new media, more people - possibly including caregivers of persons with dementia - are turning to social media platforms to share their thoughts and emotions related to personal life experiences. This may potentially serve as an opportunity to leverage on social media to gain insights into the key issues faced by dementia caregivers. We examined salient concerns of dementia caregivers through Twitter posts, aiming to shed light on how to better support and engage such caregivers.

METHOD: English tweets related to "dementia" and "caregiver" (or related terms such as "Alzheimer's disease" and "carer") were extracted between 1st January 2013 and 31st December 2022. A supervised deep learning model (Bidirectional Encoder Representations from Transformers, BERT) was trained to select tweets describing individual's accounts related to dementia caregiving. Then, an unsupervised deep learning approach (BERT-based topic modelling) was applied to identify topics from selected tweets, with each topic further grouped into themes manually using thematic analysis.

RESULT: A total of 44,527 tweets were analysed, and stratified using the emergence of COVID-19 pandemic as a threshold. Three themes were derived: challenges of caregiving in dementia, positive aspects related to dementia caregiving, and dementia-related stigmatization. Over time, there is a rising trend of tweets relating to dementia caregiving. Post-pandemic, challenges of caregiving remained the top discussed topic; followed by an increase in tweets related to dementia-related stigmatization; and a decrease in tweets related to positive aspects of caregiving (p-value<.001).

CONCLUSION: Social media is increasingly being used by dementia caregivers to share their thoughts. The findings uncover the worrying trend of growing dementia-related stigmatization among the caregivers, and its manifestation in the form of devaluing others. The persistence of these issues post-pandemic underscores the need for caregiver's support and resources.

PMID:39782315 | DOI:10.1002/alz.083965

J Magn Reson Imaging. 2025 Jan 9. doi: 10.1002/jmri.29687. Online ahead of print.

ABSTRACT

Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) is a key tool due to its substantial sensitivity for invasive breast cancers. Computer-aided detection (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas of interest, extracting quantitative features, and integrating with computer-aided diagnosis (CADx) pipelines. This review aims to provide a comprehensive overview of the current state of CADe systems in breast MRI, focusing on the technical details of pipelines and segmentation models including classical intensity-based methods, supervised and unsupervised machine learning (ML) approaches, and the latest deep learning (DL) architectures. It highlights recent advancements from traditional algorithms to sophisticated DL models such as U-Nets, emphasizing CADe implementation of multi-parametric MRI acquisitions. Despite these advancements, CADe systems face challenges like variable false-positive and negative rates, complexity in interpreting extensive imaging data, variability in system performance, and lack of large-scale studies and multicentric models, limiting the generalizability and suitability for clinical implementation. Technical issues, including image artefacts and the need for reproducible and explainable detection algorithms, remain significant hurdles. Future directions emphasize developing more robust and generalizable algorithms, integrating explainable AI to improve transparency and trust among clinicians, developing multi-purpose AI systems, and incorporating large language models to enhance diagnostic reporting and patient management. Additionally, efforts to standardize and streamline MRI protocols aim to increase accessibility and reduce costs, optimizing the use of CADe systems in clinical practice. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.

PMID:39781684 | DOI:10.1002/jmri.29687

J Magn Reson Imaging. 2025 Jan 9. doi: 10.1002/jmri.29708. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is the deadliest malignant tumor, with a grim 5-year overall survival rate of about 12%. As its incidence and mortality rates rise, it is likely to become the second-leading cause of cancer-related death. The radiological assessment determined the stage and management of PDAC. However, it is a highly heterogeneous disease with the complexity of the tumor microenvironment, and it is challenging to adequately reflect the biological aggressiveness and prognosis accurately through morphological evaluation alone. With the dramatic development of artificial intelligence (AI), multiparametric magnetic resonance imaging (mpMRI) using specific contrast media and special techniques can provide morphological and functional information with high image quality and become a powerful tool in quantifying intratumor characteristics. Besides, AI has been widespread in the field of medical imaging analysis. Radiomics is the high-throughput mining of quantitative image features from medical imaging that enables data to be extracted and applied for better decision support. Deep learning is a subset of artificial neural network algorithms that can automatically learn feature representations from data. AI-enabled imaging biomarkers of mpMRI have enormous promise to bridge the gap between medical imaging and personalized medicine and demonstrate huge advantages in predicting biological characteristics and the prognosis of PDAC. However, current AI-based models of PDAC operate mainly in the realm of a single modality with a relatively small sample size, and the technical reproducibility and biological interpretation present a barrage of new potential challenges. In the future, the integration of multi-omics data, such as radiomics and genomics, alongside the establishment of standardized analytical frameworks will provide opportunities to increase the robustness and interpretability of AI-enabled image biomarkers and bring these biomarkers closer to clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 4.

PMID:39781607 | DOI:10.1002/jmri.29708

J Surg (Lisle). 2024;9(15):11189. doi: 10.29011/2575-9760.11189. Epub 2024 Nov 27.

ABSTRACT

PURPOSE: Appropriate opioid management is crucial to reduce opioid overdose risk for ICU surgical patients, which can lead to severe complications. Accurately predicting postoperative opioid needs and understanding the associated factors can effectively guide appropriate opioid use, significantly enhancing patient safety and recovery outcomes. Although machine learning models can accurately predict postoperative opioid needs, lacking interpretability hinders their adoption in clinical practice.

METHODS: We developed an interpretable deep learning framework to evaluate individual feature's impact on postoperative opioid use and identify important factors. A Permutation Feature Importance Test (PermFIT) was employed to assess the impact with a rigorous statistical inference for machine learning models including Support Vector Machines, eXtreme Gradient Boosting, Random Forest, and Deep Neural Networks (DNN). The Mean Squared Error (MSE) and Pearson Correlation Coefficient (PCC) were used to evaluate the performance of these models.

RESULTS: We conducted analysis utilizing the electronic health records of 4,912 surgical patients from the Medical Information Mart for Intensive Care database. In a 10-fold cross-validation, the DNN outperformed other machine learning models, achieving the lowest MSE (7889.2 mcg) and highest PCC (0.283). Among 25 features, 13-including age, surgery type, and others-were identified as significant predictors of postoperative opioid use (p < 0.05).

CONCLUSION: The DNN proved to be an effective model for predicting postoperative opioid consumption and identifying significant features through the PermFIT framework. This approach offers a valuable tool for precise opioid prescription tailored to the individual needs of ICU surgical patients, improving patient outcomes and enhancing safety.

PMID:39781484 | PMC:PMC11709741 | DOI:10.29011/2575-9760.11189

Int J Clin Pediatr Dent. 2024 Nov;17(11):1289-1295. doi: 10.5005/jp-journals-10005-2971. Epub 2024 Dec 19.

ABSTRACT

AIM AND BACKGROUND: The applications of artificial intelligence (AI) are escalating in all frontiers, specifically healthcare. It constitutes the umbrella term for a number of technologies that enable machines to independently solve problems they have not been programmed to address. With its aid, patient management, diagnostics, treatment planning, and interventions can be significantly improved. The aim of this review is to analyze the current data to assess the applications of artificial intelligence in pediatric dentistry and determine their clinical effectiveness.

MATERIALS AND METHODS: A search of published studies in PubMed, Web of Science, Scopus, and Google Scholar databases was included till January 2024.

RESULTS: This review consisted of 30 published studies in the English language. The use of AI has been employed in the detection of dental caries, dental plaque, behavioral science, interceptive orthodontics, predicting the dental age, and identification of teeth which can enhance patient care.

CONCLUSION: Artificial intelligence models can be used as an aid to the clinician as they are of significant help at individual and community levels in identifying an increased risk to dental diseases.

CLINICAL SIGNIFICANCE: Artificial intelligence can be used as an asset in preventive school health programs, dental education for students and parents, and to assist the clinician in the dental practice. Further advancements in technology will give rise to newer potential innovations and applications.

HOW TO CITE THIS ARTICLE: Tanna DA, Bhandary S, Hegde SK. Tech Bytes-Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review. Int J Clin Pediatr Dent 2024;17(11):1289-1295.

PMID:39781392 | PMC:PMC11703760 | DOI:10.5005/jp-journals-10005-2971

Cureus. 2024 Dec 9;16(12):e75389. doi: 10.7759/cureus.75389. eCollection 2024 Dec.

ABSTRACT

Alzheimer's disease (AD) and other neurodegenerative illnesses place a heavy strain on the world's healthcare systems, particularly among the aging population. With a focus on research from January 2022 to September 2023, this scoping review, which adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-Scr) criteria, examines the changing landscape of artificial intelligence (AI) applications for early AD detection and diagnosis. Forty-four carefully chosen articles were selected from a pool of 2,966 articles for the qualitative synthesis. The research reveals impressive advancements in AI-driven approaches, including neuroimaging, genomics, cognitive tests, and blood-based biomarkers. Notably, AI models focusing on deep learning (DL) algorithms demonstrate outstanding accuracy in early AD identification, often even before the onset of clinical symptoms. Multimodal approaches, which combine information from various sources, including neuroimaging and clinical assessments, provide comprehensive insights into the complex nature of AD. The study also emphasizes the critical role that blood-based and genetic biomarkers play in strengthening AD diagnosis and risk assessment. When combined with clinical or imaging data, genetic variations and polygenic risk scores help to improve prediction models. In a similar vein, blood-based biomarkers provide non-invasive instruments for detecting metabolic changes linked to AD. Cognitive and functional evaluations, which include neuropsychological examinations and assessments of daily living activities, serve as essential benchmarks for monitoring the course of AD and directing treatment interventions. When these evaluations are included in machine learning models, the diagnosis accuracy is improved, and treatment monitoring is made more accessible. In addition, including methods that support model interpretability and explainability helps in the thorough understanding and valuable implementation of AI-driven insights in clinical contexts. This review further identifies several gaps in the research landscape, including the need for diverse, high-quality datasets to address data heterogeneity and improve model generalizability. Practical implementation challenges, such as integrating AI systems into clinical workflows and clinician adoption, are highlighted as critical barriers to real-world application. Moreover, ethical considerations, particularly surrounding data privacy and informed consent, must be prioritized as AI adoption in healthcare accelerates. Performance metrics (e.g., sensitivity, specificity, and area under the curve (AUC)) for AI-based approaches are discussed, with a need for clearer reporting and comparative analyses. Addressing these limitations, alongside methodological clarity and critical evaluation of biases, would strengthen the credibility of AI applications in AD detection. By expanding its scope, this study highlights areas for improvement and future opportunities in early detection, aiming to bridge the gap between innovative AI technologies and practical clinical utility.

PMID:39781179 | PMC:PMC11709138 | DOI:10.7759/cureus.75389

Biomed Eng Lett. 2024 Dec 2;15(1):37-55. doi: 10.1007/s13534-024-00436-6. eCollection 2025 Jan.

ABSTRACT

Robotic systems rely on spatio-temporal information to solve control tasks. With advancements in deep neural networks, reinforcement learning has significantly enhanced the performance of control tasks by leveraging deep learning techniques. However, as deep neural networks grow in complexity, they consume more energy and introduce greater latency. This complexity hampers their application in robotic systems that require real-time data processing. To address this issue, spiking neural networks, which emulate the biological brain by transmitting spatio-temporal information through spikes, have been developed alongside neuromorphic hardware that supports their operation. This paper reviews brain-inspired learning rules and examines the application of spiking neural networks in control tasks. We begin by exploring the features and implementations of biologically plausible spike-timing-dependent plasticity. Subsequently, we investigate the integration of a global third factor with spike-timing-dependent plasticity and its utilization and enhancements in both theoretical and applied research. We also discuss a method for locally applying a third factor that sophisticatedly modifies each synaptic weight through weight-based backpropagation. Finally, we review studies utilizing these learning rules to solve control tasks using spiking neural networks.

PMID:39781065 | PMC:PMC11704115 | DOI:10.1007/s13534-024-00436-6

Biomed Eng Lett. 2024 Oct 10;15(1):1-35. doi: 10.1007/s13534-024-00437-5. eCollection 2025 Jan.

ABSTRACT

Osteoarthritis (OA) is a musculoskeletal disorder that affects weight-bearing joints like the hip, knee, spine, feet, and fingers. It is a chronic disorder that causes joint stiffness and leads to functional impairment. Knee osteoarthritis (KOA) is a degenerative knee joint disease that is a significant disability for over 60 years old, with the most prevalent symptom of knee pain. Radiography is the gold standard for the evaluation of KOA. These radiographs are evaluated using different classification systems. Kellgren and Lawrence's (KL) classification system is used to classify X-rays into five classes (Normal = 0 to Severe = 4) based on osteoarthritis severity levels. In recent years, with the advent of artificial intelligence, machine learning, and deep learning, more emphasis has been given to automated medical diagnostic systems or decision support systems. Computer-aided diagnosis is needed for the improvement of health-related information systems. This survey aims to review the latest advances in automated radiographic classification and detection of KOA using the KL system. A total of 85 articles are reviewed as original research or survey articles. This survey will benefit researchers, practitioners, and medical experts interested in X-rays-based KOA diagnosis and prediction.

PMID:39781063 | PMC:PMC11704124 | DOI:10.1007/s13534-024-00437-5

Med Biol Eng Comput. 2025 Jan 9. doi: 10.1007/s11517-024-03264-z. Online ahead of print.

ABSTRACT

This study aimed to analyze computational techniques in ECG imaging (ECGI) reconstruction, focusing on dataset identification, problem-solving, and feature extraction. We employed a PRISMA approach to review studies from Scopus and Web of Science, applying Cochrane principles to assess risk of bias. The selection was limited to English peer-reviewed papers published from 2010 to 2023, excluding studies that lacked computational technique descriptions. From 99 reviewed papers, trends show a preference for traditional methods like the boundary element and Tikhonov methods, alongside a rising use of advanced technologies including hybrid techniques and deep learning. These advancements have enhanced cardiac diagnosis and treatment precision. Our findings underscore the need for robust data utilization and innovative computational integration in ECGI, highlighting promising areas for future research and advances. This shift toward tailored cardiac care suggests significant progress in diagnostic and treatment methods.

PMID:39779645 | DOI:10.1007/s11517-024-03264-z

J Imaging Inform Med. 2025 Jan 8. doi: 10.1007/s10278-024-01368-4. Online ahead of print.

ABSTRACT

Biopsy is considered the gold standard for diagnosing brain tumors, but its invasive nature can pose risks to patients. Additionally, tissue analysis can be cumbersome and inconsistent among observers. This research aims to develop a cost-effective, non-invasive, MRI-based computer-aided diagnosis tool that can reliably, accurately and swiftly identify brain tumor grades. Our system employs ensemble deep learning (EDL) within an MRI multiclass framework that includes five datasets: two-class (C2), three-class (C3), four-class (C4), five-class (C5) and six-class (C6). The EDL utilizes a majority voting algorithm to classify brain tumors by combining seven renowned deep learning (DL) models-EfficientNet, VGG16, ResNet18, GoogleNet, ResNet50, Inception-V3 and DarkNet-and seven machine learning (ML) models, including support vector machine, K-nearest neighbour, Naïve Bayes, decision tree, linear discriminant analysis, artificial neural network and random forest. Additionally, local interpretable model-agnostic explanations (LIME) are employed as an explainable AI algorithm, providing a visual representation of the CNN's internal workings to enhance the credibility of the results. Through extensive five-fold cross-validation experiments, the DL-based majority voting algorithm outperformed the ML-based majority voting algorithm, achieving the highest average accuracies of 100 ± 0.00%, 98.55 ± 0.35%, 98.47 ± 0.63%, 95.34 ± 1.17% and 96.61 ± 0.85% for the C2, C3, C4, C5 and C6 datasets, respectively. Majority voting algorithms typically yield consistent results across different folds of the brain tumor data and enhance performance compared to any individual deep learning and machine learning models.

PMID:39779641 | DOI:10.1007/s10278-024-01368-4

Eur Radiol. 2025 Jan 9. doi: 10.1007/s00330-024-11312-3. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic liver disease (CLD) is a substantial cause of morbidity and mortality worldwide. Liver stiffness, as measured by MR elastography (MRE), is well-accepted as a surrogate marker of liver fibrosis.

PURPOSE: To develop and validate deep learning (DL) models for predicting MRE-derived liver stiffness using routine clinical non-contrast abdominal T1-weighted (T1w) and T2-weighted (T2w) data from multiple institutions/system manufacturers in pediatric and adult patients.

MATERIALS AND METHODS: We identified pediatric and adult patients with known or suspected CLD from four institutions, who underwent clinical MRI with MRE from 2011 to 2022. We used T1w and T2w data to train DL models for liver stiffness classification. Patients were categorized into two groups for binary classification using liver stiffness thresholds (≥ 2.5 kPa, ≥ 3.0 kPa, ≥ 3.5 kPa, ≥ 4 kPa, or ≥ 5 kPa), reflecting various degrees of liver stiffening.

RESULTS: We identified 4695 MRI examinations from 4295 patients (mean ± SD age, 47.6 ± 18.7 years; 428 (10.0%) pediatric; 2159 males [50.2%]). With a primary liver stiffness threshold of 3.0 kPa, our model correctly classified patients into no/minimal (< 3.0 kPa) vs moderate/severe (≥ 3.0 kPa) liver stiffness with AUROCs of 0.83 (95% CI: 0.82, 0.84) in our internal multi-site cross-validation (CV) experiment, 0.82 (95% CI: 0.80, 0.84) in our temporal hold-out validation experiment, and 0.79 (95% CI: 0.75, 0.81) in our external leave-one-site-out CV experiment. The developed model is publicly available ( https://github.com/almahdir1/Multi-channel-DeepLiverNet2.0.git ).

CONCLUSION: Our DL models exhibited reasonable diagnostic performance for categorical classification of liver stiffness on a large diverse dataset using T1w and T2w MRI data.

KEY POINTS: Question Can DL models accurately predict liver stiffness using routine clinical biparametric MRI in pediatric and adult patients with CLD? Findings DeepLiverNet2.0 used biparametric MRI data to classify liver stiffness, achieving AUROCs of 0.83, 0.82, and 0.79 for multi-site CV, hold-out validation, and external CV. Clinical relevance Our DeepLiverNet2.0 AI model can categorically classify the severity of liver stiffening using anatomic biparametric MR images in children and young adults. Model refinements and incorporation of clinical features may decrease the need for MRE.

PMID:39779515 | DOI:10.1007/s00330-024-11312-3