Artificial Intelligence Machine Learning Research Articles

Can Artificial Intelligence Accurately Detect Urinary Stones? A Systematic Review.

Objectives: To perform a systematic review on artificial intelligence (AI) performances to detect urinary stones. Methods: A PROSPERO-registered (CRD473152) systematic search of Scopus, Web of Science, Embase, and PubMed databases was performed to identify original research articles pertaining to AI stone detection or measurement, using search terms ("automatic" OR "machine learning" OR "convolutional neural network" OR "artificial intelligence" OR "detection" AND "stone volume"). Risk-of-bias (RoB) assessment was performed according to the Cochrane RoB tool, the Joanna Briggs Institute Checklist for nonrandomized studies, and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: Twelve studies were selected for the final review, including three multicenter and nine single-center retrospective studies. Eleven studies completed at least 50% of the CLAIM checkpoints and only one presented a high RoB. All included studies aimed to detect kidney (5/12, 42%), ureter (2/12, 16%), or urinary (5/12, 42%) stones on noncontrast computed tomography (NCCT), but 42% intended to automate measurement. Stone distinction from vascular calcification interested two studies. All studies used AI machine learning network training and internal validation, but a single one provided an external validation. Trained networks achieved stone detection, with sensitivity, specificity, and accuracy rates ranging from 58.7% to 100%, 68.5% to 100%, and 63% to 99.95%, respectively. Detection Dice score ranged from 83% to 97%. A high correlation between manual and automated stone volume (r = 0.95) was noted. Differentiate distal ureteral stones and phleboliths seemed feasible. Conclusions: AI processes can achieve automated urinary stone detection from NCCT. Further studies should provide urinary stone detection coupled with phlebolith distinction and an external validation, and include anatomical abnormalities and urologic foreign bodies (ureteral stent and nephrostomy tubes) cases.

Development and validation of AI/ML derived splice-switching oligonucleotides

Splice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of functional, verifiable, and therapeutic SSOs. We trained XGboost tree models using splicing factor (SF) pre-mRNA binding profiles and spliceosome assembly information to identify modulatory SSO binding sites on pre-mRNA. Using Shapley and out-of-bag analyses we also predicted the identity of specific SFs whose binding to pre-mRNA is blocked by SSOs. This step adds considerable transparency to AI/ML-driven drug discovery and informs biological insights useful in further validation steps. We applied this approach to previously established functional SSOs to retrospectively identify the SFs likely to regulate those events. We then took a prospective validation approach using a novel target in triple negative breast cancer (TNBC), NEDD4L exon 13 (NEDD4Le13). Targeting NEDD4Le13 with an AI/ML-designed SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGFβ pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data.

Identifying necrotizing soft tissue infection using infectious fluid analysis and clinical parameters based on machine learning algorithms

BackgroundDetermining the presence of necrotizing soft tissue infection (NSTI) poses a significant hurdle. As of late, there has been a notable increase in the application of artificial intelligence (AI) machine learning techniques in identifying diseases, a shift that can be attributed to their exceptional efficiency, unbiased nature, and high precision. MethodsInformation was gathered from a cohort of 13 patients suffering from NSTI, alongside 12 patients with cellulitis. The construction of NSTI diagnostic machine learning models utilized four different algorithms, specifically random forest, k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression. These models were constructed based on 28 distinctive attributes identified through statistical examination. Following this, the diagnostic efficiency of each algorithms was evaluated. A novel random forest model, streamlined for clinical use, was later developed by focusing on 6 attributes that had the most pronounced influence on the accuracy of our initial random forest model. ResultsThe following data was noted regarding the sensitivity and specificity of the four NSTI diagnostic models:logistic regression displayed 78.2 % and 83.7 %, KNN presented 79.1 % and 87.1 %, SVM showed 83.5 % and 86.3 %, and random forest exhibited 89.6 % and 92.9 %, respectively. In comparison, lactate levels in fluid demonstrated 100 % sensitivity and 76.9 % specificity at an optimal cut-off point of 69.6 mg/dL. Among all four machine learning models, random forest outperformed the others and also showed better results than fluid lactate. A newly constructed random forest model, created using 6 of the 13 identified features, displayed promising results in diagnosing NSTI, having a sensitivity and specificity of 90.2 % and 92.2 %, respectively. ConclusionsDeveloping a diagnostic model for NSTI employing the random forest algorithm has resulted in a diagnostic technique that is more efficient, cost-effective, and expedient. This approach could provide healthcare practitioners with the tools to identify and manage NSTI with greater efficacy.

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. PRR1-10.2196/54388.

AI-Powered Revolution: Transforming Industrial Production through Automation

The integration of artificial intelligence (AI) tools has propelled automation in industrial production to new heights, ushering in a wave of transformative advancements. This research delves into the profound impact of AI tools on industrial production, elucidating their pivotal role in driving efficiency, cost reduction, and productivity enhancements. By leveraging AI algorithms and machine learning techniques, manufacturers can optimize operations with unprecedented precision and agility. However, alongside the promises of increased efficiency come significant challenges and opportunities. This paper navigates through the complexities of implementing AI-driven automation in manufacturing processes, addressing issues such as data security, workforce adaptation, and ethical considerations. Through rigorous secondary data analysis and research methodology, this study endeavours to shed light on the current state of automation in industrial production while offering valuable insights into its future prospects and implications for the manufacturing industry.

The Impact of Business Continuity on Supply Chain Practices and Resilience Due to COVID-19

Background: Business continuity entails the potential negative consequences of uncertainty on a firm’s ability to achieve strategic objectives. The COVID-19 pandemic significantly impacted business continuity due to lockdowns, travel restrictions, and social distancing measures. Consequently, firms adopted specific supply chain (SC) practices to effectively navigate this global crisis. Methods: This research adopted a stochastic approach based on Bayesian Networks to evaluate the implications of business continuity on firms’ decisions to embrace SC practices, focusing on omnichannel strategies, SC coordination, and technologies such as artificial intelligence systems, big data and machine learning, and mobile applications. Results: Our findings revealed that firms facing disruption in a single performance area can apply specific strategies to maintain resilience. However, multiple areas of underperformance necessitate a varied approach. Conclusions: According to our empirical analysis, omnichannel strategies are critical when disruptions simultaneously impact quality, inventory, sales, and ROI, particularly during major disruptions such as the COVID-19 pandemic. AI and big data become vital when multiple risks coalesce, enhancing areas such as customer service and supply chain visibility. Moreover, supply chain coordination and mobile app adoption are effective against individual performance risks, proving crucial in mitigating disruption impacts across various business aspects. These findings help policy-makers and business owners to have a better understanding of how business continuity based on performance resistance to disruptions pushes companies to adopt different practices including new technologies and supply chain coordination. Accordingly, they can use the outputs of this study to devise strategies for improving resilience considering their supply chain vulnerabilities.

Leveraging Modeling and Simulation to Enhance the Efficiency of Bioequivalence Approaches for Generic Drugs: Highlights from the 2023 Generic Drug Science and Research Initiatives Public Workshop.

The 2023 Generic Drug Science and Research Initiative Public Workshop organized by the U.S. Food and Drug Administration (FDA) discussed the research needs to improve and enhance bioequivalence (BE) approaches for generic drug development. FDA takes such research needs and panel discussions into account to develop its Generic Drug User Fee Amendments III (GDUFA III) Science and Research Initiatives specific to generics. During the five workshop sessions, presentations and panel discussions focused on identifying and addressing scientific gaps and research needs related to nitrosamine impurity issues, BE assessment for oral products, innovative BE approaches for long-acting injectable products, alternative BE approaches for orally inhaled products, and advanced BE methods for topical products. Specifically, this report highlights the discussions on how to improve BE assessment for developing generic drug products based on research priorities for leveraging quantitative methods and modeling, as well as artificial intelligence/machine learning (AI/ML).

Abstract LB122: JNJ-87890387, a novel ENPP3 bispecific T-cell redirector (ENPP3xCD3) with tumor selectivity through targeting apical surface antigens

Abstract Clinical success of T-cell redirection therapy for solid tumors has been limited potentially due to a lack of target specificity against cancer, leading to on-target off-tumor toxicity at sub-efficacious doses. ENPP3 (ectonucleotide pyrophosphatase/phosphodiesterase family member 3) is a transmembrane protein that was identified with the help of an artificial intelligence machine learning algorithm. ENPP3 is characterized by apically restricted expression in most normal tissues and elevated, depolarized expression in several solid tumors, thus potentially providing tumor selectivity. ENPP3 expression exhibits a high prevalence in several solid tumors including renal cell carcinoma (RCC) - clear cell (93%) and papillary (78%), lung adenocarcinoma (50%), endometrioid uterine (53%) and ovarian (47%) cancers, colorectal carcinoma (51%), and hepatocellular carcinoma (HCC, 47%). JNJ-87890387 is a fully human bispecific antibody with high affinity and specific binding to ENPP3 on tumor cells and lower affinity binding to cluster of differentiation (CD)3 on T-cells. In vitro, JNJ-87890387 demonstrated potent (pM) T-cell activation and cytotoxicity against tumor cell lines with different levels of endogenous ENPP3 membrane expression, and absence of killing against cells lacking ENPP3 expression, confirming JNJ-87890387 specificity. In vivo, JNJ-87890387 demonstrated ENPP3-expression-dependent potent anti-tumor activity in multiple cell line-derived and patient tumor-derived RCC and HCC xenograft models with complete tumor regressions, and evidence of dose-dependent CD4+ and CD8+ T cell infiltration into the tumors. Preclinical safety assessment in the cynomolgus monkey with a tool molecule using the therapeutic ENPP3 binding arm but with a cynomolgus CD3 cross-reactive arm only identified toxicities consistent with the modality. In summary, these data support JNJ-87890387’s clinical development with an ongoing first-in-human Phase I study to evaluate the safety and preliminary anti-tumor activity in advanced-stage solid tumors with a high prevalence of ENPP3 expression. Citation Format: Smruthi Vijayaraghavan, Pankaj Seth, Annmarie Winkis, Kristen Chevalier, Alexa Marthaler, Katrin Sproesser, Vince Torti, Nirav Shah, Eilyn Lacy, Anna-Lena Frisk, Heather Deutsch, Gerald Chu, Michael Sharp, Jonathan Pabalan, Cholpon Tilegenova, Katherine Thorton, Josh Lauring, Bethany Mattson, Ken Tian, Jacqueline Kinyamu-Akunda, Laurie Lenox, Wan Cheung Cheung, Sheri Moores, Sylvie Laquerre. JNJ-87890387, a novel ENPP3 bispecific T-cell redirector (ENPP3xCD3) with tumor selectivity through targeting apical surface antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB122.

인공지능 기반의 자동화된 통합보안관제시스템 모델 연구

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.

Abstract 4915: Identification and validation of novel prognostic genetic markers in HER2-negative advanced gastric cancer (AGC) by artificial intelligence (AI) deep learning and machine learning algorithm

Abstract Introduction: Despite advances in anticancer treatments, there has been urgent need to identify novel prognostic markers for HER2-negative AGC. We propose AI based deep learning and machine learning pipeline to explore genetic markers that predict patient response to chemotherapy in HER2-negative AGC. Methods: We retrospectively assessed 179 stage IV HER2-negative AGC patients who have been treated with first-line chemotherapy in Yonsei Cancer Center, Korea between 2015 and 2021. In-house targeted sequencing panel data (CancerSCANTM and CancerMaster) were used to identify candidate prognostic markers. DeepSurv was mainly used to analyze the progression-free survival (PFS), which is a deep learning algorithm that can investigate prognostic roles. The SHapley Additive exPlanations (SHAP) method was applied to open the deep learning models’ black-box and calculate the importance ranking of gene features. Machine learning models-random survival forest, elastic-net, and lasso-were also investigated to identify potentially meaningful gene variants. To rank the gene features, we calculated variable importance (VIMP) for random survival forest, and penalized regression coefficients for elastic-net and lasso. All deep learning and machine learning models were trained using 5-folds cross-validation in 1,000 iterations of re-sampling bootstrapping with hyperparameter tuning by grid search. To classify responder or non-responder related genetic markers to chemotherapy, we used the signs of averaged penalize coefficients obtained from elastic-net and lasso. Based on the median linear predictor with candidate gene features, patients were classified into high-risk and low-risk group. Results: A total of 7,824 common variants were analyzed and 20 prognostic genetic markers were identified based on the top average values of SHAP, VIMP, and penalized regression coefficients. The top 10 non-responder genetic markers are included as follows: PARP4(p.S873N), CHUK(p.V268I), BARD1(359_365del), CREBBP(p.Y1125F), BARD1(p.P24S), PHLPP2(p.R1312Q), FAT3(p.Q3375R), CASP5(p.T106A), PHLPP2(p.I544V), and PTCH2(p.T988M). Otherwise, the top 10 responder genetic markers are defined as follows: IL7R(p.I66T), NOTCH4(L16delinsLL), FGFR3, BCL2A1(p.G82D), ZNF217(p.T548I), BRCA1(p.K1183R), FANCA(p.A412V), ADGRA2, CDKN2B, and FANCM(p.S175F). The high-risk group had worse PFS compared with low-risk group (median PFS 4.1 vs. 9.1 months; P-value <0.0001). Conclusion: This study showed the potential of AI deep learning and machine learning pipeline that employs an innovate prognostic genetic markers for HER2-negative AGC. Current ongoing analyses of larger cohort with more comprehensive clinical data will be presented. Citation Format: Sejung Park, Seok-Jae Heo, Choong-Kun Lee, Yaeji Lee, Woo Sun Kwon, Jingmin Che, Minseok Kim, Hyun Cheol Chung, Sun Young Rha. Identification and validation of novel prognostic genetic markers in HER2-negative advanced gastric cancer (AGC) by artificial intelligence (AI) deep learning and machine learning algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4915.

PersistVerify: Federated model ownership verification with spatial attention and boundary sampling

Federated learning, known for its emphasis on privacy and resource efficiency, has emerged as a transformative paradigm in the fields of artificial intelligence and industrial machine learning. However, with the advancement and widespread adoption of this technology, the concern of model theft in federated learning models becomes increasingly prominent. While current endeavors concentrate on protecting individual deep learning models, limited attention has been dedicated to those involved in federated learning, especially within decentralized environments. To fill this void, we introduce PersistVerify, an innovative approach for verifying model copyright within the federated learning framework. PersistVerify integrates boundary sample selection and spatial attention mechanisms, enhancing the robustness and confidentiality of neural network backdoor watermarks to ensure secure verification of model ownership. PersistVerify constructs a confidential and robust watermark dataset through three sequential steps: latent representation extraction, boundary sample selection, and spatial attention-based trigger embedding. Our research addresses the pervasive challenge of reliable model ownership verification in federated learning, demonstrating the efficacy of PersistVerify. This study provides valuable insights and methodologies to maintain the confidentiality and reliability of neural network backdoor watermarks in the domains of artificial intelligence and federated learning.

Undergraduate and graduate students' conceptual understanding of model classification outcomes under the lens of scientific argumentation

AbstractRecent advancements in artificial intelligence (AI) and machine learning (ML) have driven research and development across multiple industries to meet national economic and technological demands. Consequently, companies are investing in AI, ML, and data analytics workforce development efforts to digitalize operations and enhance global competitiveness. As such, evidence‐based educational research around ML is essential to provide a foundation for the future workforce as they face complex AI challenges. This study explored students' conceptual ML understanding through a scientific argumentation framework, where we examined how they used evidence and reasoning to support claims about their ML models. This framework lets us gain insight into students' conceptualizations and helped scaffold student learning via a cognitive apprenticeship model. Thirty students in a mechanical engineering classroom at Purdue University experimented with neural network ML models within a computational notebook to create visual claims (ML models) with textual explanations of their evidence and reasoning. Accordingly, we qualitatively analyzed their learning artifacts to examine their underfit, fit, and overfit models and explanations. It was found that some students tended toward technical explanations while others used visual explanations. Students with technically dominant explanations had higher proficiency in generating correctly fit models but lacked explanatory evidence. Conversely, students with visually dominant explanations provided evidence but lacked technical reasoning and were less accurate in identifying fit models. We discuss implications for both groups of students and offer future research directions to examine how positive pedagogical elements of learning design can optimize ML educational material and AI workforce development.

Artificial intelligence in positive mental health: a narrative review.

The paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role in mental health. AI has a huge number of promises to offer mental health care and this paper looks at multiple facets of the same. The paper first defines AI and its scope in the area of mental health. It then looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI. The role of AI in various psychiatric disorders like neurodegenerative disorders, intellectual disability and seizures are discussed along with the role of AI in awareness, diagnosis and intervention in mental health disorders. The role of AI in positive emotional regulation and its impact in schizophrenia, autism spectrum disorders and mood disorders is also highlighted. The article also discusses the limitations of AI based approaches and the need for AI based approaches in mental health to be culturally aware, with structured flexible algorithms and an awareness of biases that can arise in AI. The ethical issues that may arise with the use of AI in mental health are also visited.

Prediction of Intermittent Demand Occurrence using Machine Learning

Demand forecasting plays a pivotal role in modern Supply Chain Management (SCM). It is an essential part of inventory planning and management and can be challenging at times. One of the major issues being faced in demand forecasting is insufficient forecast accuracy to predict the expected demand and fluctuation in actual vs. the predicted demand results in fore-casting errors. This problem is further exaggerated with slow-moving and intermittent demand items. Every organization encounters large proportions of items that have small ir-regular demand with long periods of zero demand, which are known as intermittent demand Items. Demand for such items occur sporadically and with considerable fluctuation in the size of the demand. Forecasting of the intermittent demand entails the prediction of demand series that is characterized by the time interval between demand being significantly greater than the unit forecast period. Because of this there are multiple periods of no demand in the intermittent demand time series. The challenge with these products with low irregular demand is that these items need to be stocked and replenished at regular interval irrespective of the demand cycle, thus adding to the cost of holding the inventory. Since the demand is not continuous, Traditional Forecasting models are unable to provide reliable estimate of required inventory level and replenishment point. Forecast errors would resulting in obsolescent stock or unfulfilled demand. The current paper presents a simple yet powerful approach for generating a demand forecasting and replenishment process for such low volume intermittent demand items to come up with a recommendation for dynamic re-order point, thus, improving the inventory performance of these items. Currently, the demand forecast is generally based on past usage patterns. The rise of Artificial Intelligence/Machine Learning (AI/ML) has provided a strong alternative to solve the problem of forecasting Intermittent Demand. The intention is to highlight that machine learning algorithm is more efficient and accurate than traditional forecasting method. As we move forward to industry 4.0, the digital supply chain is considered as the most essential com-ponent of the value chain wherein the inventory size is controlled, and the demand predicted.

The drift handling framework for open radio access networks: An experimental evaluation

The Open Radio Access Network (i.e., Open RAN) aims to transform the inflexible, proprietary RAN into one that is flexible, programmable, and disaggregated to provide intelligent solutions at the network edge for delivering the highly dynamic service demands of Beyond 5G (B5G) networks. Open RAN Working Group-2 (WG2) focuses on the architecture and standards of Artificial Intelligence/Machine Learning (AI/ML) to meet the Service Level Agreement (SLA) requirements for emerging use cases. The inherent dynamic nature of B5G networks and their applications often leads to AI/ML model performance degradation (i.e., drift), resulting in violations of SLAs, over- or under-provisioning of resources, etc.This paper proposes a drift handling framework which includes drift detection, analysis, and its adaptation to minimize the SLA violations and efficient resource utilization. The proposed drift handling framework is evaluated across three diverse use cases: (i) Quality of Service prediction, (ii) Channel Quality Indicator prediction, and (iii) User traffic prediction. The experimental evaluations are performed by using the Open RAN Software Community, and OpenAirInterface FlexRIC platforms with a real-time dataset. Results demonstrate that the proposed drift handling framework outperforms all other considered state-of-the art frameworks for all the scenarios in terms of the drift detection time, accuracy, precision, F1-score, etc.

The role of bank–FinTech partnerships in creating a more inclusive banking system

FinTech firms are often viewed as competing with banks. Instead, more recently, there has been increased partnership and collaboration between FinTech firms and banks. These partnerships have allowed banks to access more information on consumers through data aggregation, artificial intelligence/machine learning (AI/ML) and other tools. This paper explores explore the demographics of consumers targeted by banks that have entered into such partnerships. Specifically, the paper tests whether banks are more likely to extend credit offers (by mail) and/or credit originations to consumers who would have otherwise been deemed high risk, either because of low credit scores or lack of credit scores altogether. The paper uses data on credit offers based on a survey conducted by Mintel, as well as data on credit originations based on the Federal Reserve’s Y-14M reports. Additionally, the paper analyses a unique data set of partnerships between FinTech firms and banks compiled by CB Insights to identify the relevant partnerships. The paper concludes that banks are more likely to offer credit cards and personal loans (and grant larger credit limits) to the credit-invisible and below-prime consumers after the FinTech partnership period. Similarly, it finds that FinTech partnerships result in banks being more likely to originate mortgage loans (and grant larger loan amount) to nonprime homebuyers. Overall, the paper concludes that bank/FinTech partnerships could help to move towards a more inclusive financial system.

Discovery and learning of feature geometry in underwater acoustic sensing: Case studies and challenges in active sonar applications

Interpretable artificial intelligence (AI) and related machine learning (ML) techniques are gaining popularity for underwater acoustic sensing applications. However, interpretability of machine learnt features poses a fundamental challenge to successful application of these powerful data science techniques to underwater acoustics. Autonomous sonar target recognition is especially interesting from the data science perspective as beyond target-specific features, a sonar ping response typically includes significant interference from the environment, e.g., clutter, multipath scattering, etc. Such environmental interference, resulting from complex, dynamic, unpredictable, and often unknown factors, manifest as identifiable structures in acoustic color or alternate multi-dimensional feature representations that can lead to machine learning and classification errors. In this talk, we will discuss these challenges commonly encountered in underwater acoustics using case studies from field experiments in active sonar target recognition. Some results will also include physics-driven simulations in this domain to provide robust ground truths. In particular, we will posit how braided feature geometry and its representation, as well geometry of overlap between features can render a sonar target feature informative, explainable, and discoverable. [Work funded by the ONR Grant Nos. N000142112420 and N000142312503 and DoD Navy (NEEC) Grant No. N001742010016.]

Harmonising linguistic validation with AI: Precision, efficiency, and the human touch in patient-reported outcome translation

The surge in artificial intelligence (AI) prompts a reassessment of linguistic validation methods for patient-reported outcome (PRO) measures. The robust linguistic process, designed to adapt PRO measures for different cultures and languages, is upheld by regulators and the outcomes research community for its value in maintaining concept equivalence across global trial data. Its methods are entrenched in human translation and review, making it more challenging to integrate AI (machine learning, deep learning, natural language processing) compared to other parts of the global localisation industry. This article provides an overview of the key challenges in integrating linguistic validation and AI. Despite these hurdles, it advocates for the industry to embrace the potential benefits through collaborative and responsible innovation.

Training self-learning circuits for power-efficient solutions

As the size and ubiquity of artificial intelligence and computational machine learning models grow, the energy required to train and use them is rapidly becoming economically and environmentally unsustainable. Recent laboratory prototypes of self-learning electronic circuits, such as “physical learning machines,” open the door to analog hardware that directly employs physics to learn desired functions from examples at a low energy cost. In this work, we show that this hardware platform allows for an even further reduction in energy consumption by using good initial conditions and a new learning algorithm. Using analytical calculations, simulations, and experiments, we show that a trade-off emerges when learning dynamics attempt to minimize both the error and the power consumption of the solution—greater power reductions can be achieved at the cost of decreasing solution accuracy. Finally, we demonstrate a practical procedure to weigh the relative importance of error and power minimization, improving the power efficiency given a specific tolerance to error.

Application of machine learning constructs to predict post-operative complications and adverse events following shoulder hemiarthroplasty

Background: Artificial intelligence (AI) constructs and machine learning (ML) algorithms have demonstrated utility in predicting various clinical, surgical, and financial outcomes. In this study, we applied AI to shoulder hemiarthroplasty (HA) to predict various post-operative complications. Methods: The sample was queried from the American college of surgeons-national surgical quality improvement program (ACS-NSQIP) database for all shoulder HA cases from 2008-2018. Six ML algorithms-random forest classifier, gradient boosting classifier, decision tree classifier, SVM classifier-tuned model, Gaussian Naïve Bayes classifier, multi-layer perception-analyzed the sample dataset. Postoperative complications included extended length of stay, non-home discharge destination, transfusion, and any adverse event. Each ML model was compared to logistic regression (LR), and model strength was evaluated. Results: We identified a total of 1585 shoulder HA cases. Mean age, BMI, operative time, and length of stay were 66±12 years, 31±8 kg/m2, 114±61 minutes, and 2.93±6.61 days. Preop hematocrit, longer operative time, and older age were most predictive of extended length of stay. Preop hematocrit, operative time, and ASA class had the highest importance in any adverse events (AAE) prediction. ML models outperformed traditional comorbidity indices, LR, for predicting extended length of stay (79% vs. 66%), non-home discharge destination (79% vs. 65%), any adverse event (78% vs. 66%), and transfusion requirement (82% vs. 63%). Conclusions: ML algorithms predicted post-surgical outcomes of interest following shoulder HA at a higher rate to conventional LR and can assist orthopedic surgeons in decision making.