Potential applications of artificial intelligence and machine learning on diagnosis, treatment, and outcome prediction to address health care disparities of chronic limb-threatening ischemia

Bagheri, Amir Behzad; Rouzi, Mohammad Dehghan; Koohbanani, Navid Alemi; Mahoor, Mohammad H.; Finco, MG; Lee, Myeounggon; Najafi, Bijan; Chung, Jayer

doi:10.1053/j.semvascsurg.2023.06.003

Cited by 20 publications

(6 citation statements)

References 27 publications

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“…Machine Learning (ML) is a subset of Arti cial Intelligence (AI) that focuses on developing computer algorithms that can learn and improve from experience. It uses statistical methods to enable machines to improve with experience [9]. Deep Learning (DL) is a subset of ML that involves algorithms inspired by the structure and function of the brain called Arti cial Neural Networks (ANN) [10].…”

Section: Arti Cial Intelligence and Natutal Language Processingmentioning

confidence: 99%

A novel approach to voice of customer extraction using GPT-3.5 Turbo: linking advanced NLP and Lean Six Sigma 4.0

Shahin,

Chen,

Hosseinzadeh

et al. 2024

Int J Adv Manuf Technol

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This research breaks new ground by utilizing the advanced natural language processing (NLP) capabilities of OpenAI's GPT-3.5 Turbo model for the extraction of Voice of Customer (VoC) data from online customer support interactions on Twitter. Traditional methods of VoC extraction have typically fallen short in capturing the richness and nuance of customer sentiment. Contemporary Machine Learning (ML) approaches, while improved, still struggle to interpret the contextual subtleties of digital customer communications effectively. This study showcases the innovative deployment of GPT-3.5 Turbo, demonstrating its superior performance in extracting VoC through a deeper understanding of conversational context and a more intuitive, chat-based data processing. Furthermore, the large-scale, multilingual processing capabilities of this model offer a more comprehensive and inclusive analysis of VoC. The study ties these advancements to Lean Six Sigma 4.0, illustrating how the integration of GPT-3.5 Turbo's transformative capabilities can elevate the customer-centric approach of Lean Six Sigma in the era of Industry 4.0. This innovative exploration points to a signi cant evolution in VoC analysis, offering potential for more insightful, real-time data-driven customer service strategies and a more substantial foundation for decision-making in product development and process improvement. Future research is encouraged to validate these preliminary ndings and to investigate ethical considerations associated with the use of such advanced NLP models.

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Section: Arti Cial Intelligence and Natutal Language Processingmentioning

confidence: 99%

A novel approach to voice of customer extraction using GPT-3.5 Turbo: linking advanced NLP and Lean Six Sigma 4.0

Shahin,

Chen,

Hosseinzadeh

et al. 2024

Int J Adv Manuf Technol

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“…Artificial intelligence and machine learning techniques are increasingly influencing diverse medical fields, from subtle applications in conditions like chronic limb-threatening ischemia [30] to more pronounced impacts in areas like speech analysis [31]. Among these AI techniques, Random Forest modeling approach uses ensemble learning to provide solutions to complex problems by combining multiple classifiers [32,33].…”

Section: Random Forest Model Approach For Feature Ranking and Trainingmentioning

confidence: 99%

Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults

Cay,

Pfeifer,

Lee

et al. 2024

Gerontology

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Introduction: Current cognitive assessments suffer from floor/ceiling and practice effects, poor psychometric performance in mild cases, and repeated assessment effects. This study explores the use of digital speech analysis as an alternative tool for determining cognitive impairment. The study specifically focuses on identifying the digital speech biomarkers associated with cognitive impairment and its severity. Methods: We recruited older adults with varying cognitive health. Their speech data, recorded via a wearable-microphone during the reading aloud of a standard passage, were processed to derive digital biomarkers such as timing, pitch, and loudness. Cohen's D effect size highlighted group differences, and correlations were drawn to the Montreal Cognitive Assessment (MoCA). A stepwise approach using a Random Forest model was implemented to distinguish cognitive states using speech data and predict MoCA scores based on highly correlated features. Results: The study comprised 59 participants, with 36 demonstrating cognitive impairment and 23 serving as cognitively intact controls. Among all assessed parameters, similarity, as determined by Dynamic Time Warping (DTW), exhibited the most substantial positive correlation (rho=0.529, p<0.001), while timing parameters, specifically the ratio of extra words, revealed the strongest negative correlation (rho=-0.441, p<0.001) with MoCA scores. Optimal discriminative performance was achieved with a combination of four speech parameters: total pause time, speech to pause ratio, similarity via DTW, and ratio of extra words. Precision and balanced accuracy scores were found to be 84.3 ± 1.5% and 75.0 ± 1.4%, respectively. Discussion: Our research proposes that reading-derived speech data facilitates the differentiation between cognitively impaired individuals and cognitively intact, age-matched older adults. Specifically, parameters based on timing and similarity within speech data provide an effective gauge of cognitive impairment severity. These results suggest speech analysis as a viable digital biomarker for early detection and monitoring of cognitive impairment, offering novel approaches in dementia care.

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“…To augment the dataset, new images were created by altering spatial characteristics, including horizontal and vertical flips, rotations, changes in image brightness, and shifts in both horizontal and vertical directions, and adjusting the magnification of existing images. DL models, with their numerous hidden neurons, depend on both the diversity and the volume [97] of the dataset utilized in training to attain high efficiency in intricate tasks [98,99]. Furthermore, data augmentation is beneficial for simulating real-world applications, as it allows capturing images from various angles and perspectives, occasionally even in inverted forms, under different conditions and using varying camera specifications.…”

Section: Datasetmentioning

confidence: 99%

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Shahin,

Chen,

Maghanaki

et al. 2024

Sensors

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Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

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Potential applications of artificial intelligence and machine learning on diagnosis, treatment, and outcome prediction to address health care disparities of chronic limb-threatening ischemia

Cited by 20 publications

References 27 publications

A novel approach to voice of customer extraction using GPT-3.5 Turbo: linking advanced NLP and Lean Six Sigma 4.0

A novel approach to voice of customer extraction using GPT-3.5 Turbo: linking advanced NLP and Lean Six Sigma 4.0

Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

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