A CRITICAL REVIEW OF AI-DRIVEN STRATEGIES FOR ENTREPRENEURIAL SUCCESS

International Journal of Management & Entrepreneurship Research, Journal Year: 2024, Volume and Issue: 6(1), P. 200 - 215

Published: Jan. 25, 2024

In the rapidly evolving landscape of entrepreneurship, integration Artificial Intelligence (AI) has emerged as a transformative force, reshaping traditional business paradigms and offering unprecedented opportunities for success. This paper provides comprehensive critical review AI-driven strategies employed by entrepreneurs to enhance their ventures. The encompasses thorough analysis key AI applications, impact on various aspects potential benefits challenges associated with implementation. first section explores role in market analysis, highlighting how advanced data analytics predictive modelling contribute informed decision-making forecasting. discussion then extends innovations product development, emphasizing acceleration ideation, prototyping, customization through machine learning algorithms. Next, scrutinizes influence customer engagement relationship management. It delves into personalized experiences facilitated chatbots, recommendation systems, sentiment while also addressing ethical considerations surrounding privacy algorithmic biases. Entrepreneurial operations efficiency gains are examined subsequent section, AI's supply chain management, logistics, resource optimization. underscores increased productivity cost-effectiveness implementation AI-powered automation smart systems. Despite myriad advantages, critically examines such concerns, job displacement, digital divide. emphasizes need balanced approach that addresses societal adoption fostering inclusive entrepreneurial ecosystems. conclusion, this not only overview current entrepreneurship but offers insights future developments challenges. Entrepreneurs, policymakers, researchers can leverage navigate intersection sustainable ethically sound environment success era. Keywords: (AI), Entrepreneurship, Strategic Implementation, Innovation, Market Analysis, Predictive Modelling.

Language: Английский

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AI supported fetal echocardiography with quality assessment

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 9, 2024

Abstract This study aimed to develop a deep learning model assess the quality of fetal echocardiography and perform prospective clinical validation. The was trained on data from 18–22-week anomaly scan conducted in seven hospitals 2008 2018. Prospective validation involved 100 patients two hospitals. A total 5363 images 2551 pregnancies were used for training model's segmentation accuracy depended image measured by score (QS). It achieved an overall average 0.91 (SD 0.09) across test set, with having above-average QS scoring 0.97 0.03). During 192 images, clinicians rated 44.8% 9.8) as equal quality, 18.69% 5.7) favoring auto-captured 36.51% 9.0) preferring manually captured ones. Images above showed better agreement segmentations ( p < 0.001) medicine experts. Auto-capture saved additional planes beyond protocol requirements, resulting more comprehensive echocardiographies. Low had adverse effect both performance clinician’s feedback. findings highlight importance developing evaluating AI models based ‘noisy’ real-life rather than pursuing highest possible retrospective academic-grade data.

Language: Английский

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Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis

Bioengineering, Journal Year: 2025, Volume and Issue: 12(3), P. 288 - 288

Published: March 13, 2025

The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, future directions AI technologies, particularly machine learning (ML) its subset, deep (DL), in diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), significantly improved image acquisition, quality assessment, objective disease diagnosis. AI-driven solutions now facilitate automated analysis, intelligent assistance, education, enabling precise lesion detection across various organs while reducing physician workload. AI’s error capabilities further enhance accuracy. Looking ahead, with is expected to deepen, promoting trends standardization, personalized treatment, healthcare, underserved areas. Despite potential, comprehensive assessments ethical implications remain limited, necessitating rigorous evaluations ensure effectiveness practice. provides a systematic evaluation technologies medicine, highlighting their transformative potential improve global healthcare outcomes.

Language: Английский

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RTSeg-net: A lightweight network for real-time segmentation of fetal head and pubic symphysis from intrapartum ultrasound images

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 175, P. 108501 - 108501

Published: April 22, 2024

Language: Английский

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PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: May 2, 2024

Abstract During the process of labor, intrapartum transperineal ultrasound examination serves as a valuable tool, allowing direct observation relative positional relationship between pubic symphysis and fetal head (PSFH). Accurate assessment descent prediction most suitable mode delivery heavily rely on this relationship. However, achieving an objective quantitative interpretation images necessitates precise PSFH segmentation (PSFHS), task that is both time-consuming demanding. Integrating potential artificial intelligence (AI) in field medical image segmentation, development evaluation AI-based models significantly access to comprehensive meticulously annotated datasets. Unfortunately, publicly accessible datasets tailored for PSFHS are notably scarce. Bridging critical gap, we introduce dataset comprising 1358 images, at pixel level. The annotation adhered standardized protocols involved collaboration among experts. Remarkably, stands expansive resource date.

Language: Английский

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Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects

Anesthesia & Analgesia, Journal Year: 2023, Volume and Issue: 137(4), P. 830 - 840

Published: Sept. 5, 2023

Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. vision–based techniques are already in clinical radiology, ophthalmology, dermatology, where some applications currently equal exceed performance specialty physicians areas interpretation. While machine anesthesia has many potential applications, its development remains infancy our specialty. Early research for focused on automated recognition anatomical structures during ultrasound-guided regional line insertion; glottic opening vocal cords laryngoscopy; prediction difficult airway using facial images; alerts endobronchial intubation detected chest radiograph. Current measuring distance between endotracheal tube tip carina have demonstrated noninferior compared board-certified physicians. The uses will only grow with advancement underlying algorithm technical developed outside medicine, such as convolutional neural networks transfer learning. This article summarizes recently published works interest, provides a brief overview used create explains frequently terms, discusses challenges encounter we embrace advantages that this technology may bring future practice patient care. As emerges onto stage, it is critically important anesthesiologists prepared confidently assess which these devices safe, appropriate, added value

Language: Английский

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Development of Artificial Intelligence Image Classification Models for Determination of Umbilical Cord Vascular Anomalies

Journal of Ultrasound in Medicine, Journal Year: 2024, Volume and Issue: 43(5), P. 881 - 897

Published: Jan. 26, 2024

The goal of this work was to develop robust techniques for the processing and identification SUA using artificial intelligence (AI) image classification models.

Language: Английский

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The role of Artificial Intelligence in detecting breast lesions using ultrasound

Quality in Sport, Journal Year: 2025, Volume and Issue: 37, P. 57301 - 57301

Published: Jan. 14, 2025

Introduction and objective: Breast cancer is the most diagnosed second leading cause of deaths in women globally, with rising cases mortality. Early detection via mammography, ultrasound, or MRI vital, ultrasound excelling dense breast tissue due to its safety accuracy.Review methods: A literature review utilizing databases like Scopus, Google Scholar, PubMed, keywords such as "AI use radiology" "BI-RADS scale" underscores need for advancements understanding managing graft rejection.Brief knowledge status: AI develops systems that simulate human intelligence, imaging by detecting patterns providing accurate results. Machine learning (ML) deep (DL) drive advances, DL's CNNs image analysis. aids BI-RADS lesion classification, detection, lymph node analysis, treatment response prediction, often surpassing radiologists. Its future relies on real-world validation, improved outcomes, clinical integration.Discussion: The integration artificial intelligence (AI) into marks a transformative leap diagnostic radiology, enhancing precision, efficiency, scalability. Driven machine (DL), excels analyzing complex datasets. However, adoption requires addressing key considerations nuanced approach.Summary: In conclusion, holds immense promise imaging, poised redefine field through enhanced capabilities utility. Continued validation efforts will ensure broader acceptance sustained impact medical imaging.

Language: Английский

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FNBUI-NET: A multi-task model for fetal nasal bone ultrasound image defect detection and classification

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 104, P. 107586 - 107586

Published: Jan. 28, 2025

Language: Английский

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FHD deep learning prognosis approach: Early detection of fetal heart disease (FHD) using ultrasonography image-based IROI combined multiresolution DCNN

Technology and Health Care, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 26, 2025

Fetal Heart Disease (FHD) is the most prevalent root cause of infant demise which accounts for 21% all congenital abnormalities, with instances being catastrophic, thereby rendering need early prognosis. Ultrasonography forefront imaging modality assessing fetal growth in four-chamber and blood vessel malformation. Clinically diagnosing abnormality time-consuming requires skill a radiologist. In subsequent, numerous preceding research strategies ideal to meta-heuristic deep learning's Faster Artificial Neural Network (FANN), Dense Recurrent (DRNN), Mask-Regional Convolution (M RCNN) Enhanced Deep Learning-assisted CNN aid identification FHD. However, prediction models have encountered multiple challenges owing imprecise hinders irrelevant adhesion. Hence, we propose automated hierarchical network-driven findings FHD vessels using ultrasonic 2D undergoes 3 consequential processes Enhanced-Adaptive Median Filtering (EAMF) pre-process concerning noise variations i.e., test SNR distortion image enhancement visual quality, Intensified Region Interest (IROI) segmentation exploiting feature selection via spatial mask-labeling Multiresolution Convolutional (MDCNN) classification detection diseased pattern confusion metrics (CM). The lesion CM determined MATLAB R2023b an overall substantial efficiency 99.79% both normal abnormal conditions significant potential assist cardiologists prognosis

Language: Английский

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