Deep Learning Model Based on Contrast-Enhanced Computed Tomography Imaging to Predict Postoperative Early Recurrence after the Curative Resection of a Solitary Hepatocellular Carcinoma

Cancers, Год журнала: 2023, Номер 15(7), С. 2140 - 2140

Опубликована: Апрель 4, 2023

We aimed to develop the deep learning (DL) predictive model for postoperative early recurrence (within 2 years) of hepatocellular carcinoma (HCC) based on contrast-enhanced computed tomography (CECT) imaging. This study included 543 patients who underwent initial hepatectomy HCC and were randomly classified into training, validation, test datasets at a ratio 8:1:1. Several clinical variables arterial CECT images used create models recurrence. Artificial intelligence implemented using convolutional neural networks multilayer perceptron as classifier. Furthermore, Youden index was discriminate between high- low-risk groups. The importance values each explanatory variable calculated permutation importance. DL developed with area under curve 0.71 (test datasets) 0.73 (validation datasets). Postoperative incidences in groups 73% 30%, respectively (p = 0.0057). Permutation demonstrated that among variables, highest value imaging analysis. predict DL-based analysis is effective determining treatment strategies HCC.

Язык: Английский

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Potential Applications of Artificial Intelligence and Machine Learning in Spine Surgery Across the Continuum of Care

The International Journal of Spine Surgery, Год журнала: 2023, Номер 17(S1), С. S26 - S33

Опубликована: Июнь 1, 2023

The worlds of spinal surgery and computational science are intersecting at the nexus operating room across continuum patient care. As medicine moves toward digitizing all aspects a patient's care, immense amounts data generated aggregated surgeons, procedures, institutions will enable previously inaccessible computationally driven insights. These early insights from artificial intelligence (AI) machine learning (ML)-enabled technologies beginning to transform surgery. complex pathologies facing spine surgeons their patients require integrative, multimodal, data-driven management strategies. these technological tools process them become increasingly available AI ML methods inform selection, preoperatively risk-stratify based on myriad factors, interoperative surgical decisions. Once enter clinical practice, use creates virtual flywheel whereby generates additional that further accelerate evolution "knowledge" systems. At this digital crossroads, interested motivated have an opportunity understand technologies, guide application optimal advocate for opportunities where powerful new can deliver step changes in efficiency, accuracy, intelligence. In present article, we review nomenclature basics highlight current future applications care

Язык: Английский

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Advanced Medical Image Segmentation Enhancement: A Particle Swarm Optimization-Based Histogram Equalization Approach

Опубликована: Янв. 2, 2024

Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study on the efficacy of Particle Swarm Optimization (PSO) combined with Histogram Equalization (HE) preprocessing segmentation, focusing Lung CT-Scan Chest X-ray datasets. Best Cost values reveal PSO algorithm’s performance, HE demonstrating significant stabilization enhanced convergence, particularly complex images. Evaluation metrics, including Accuracy, Precision, Recall, F-Score, Specificity, Dice, Jaccard, show substantial improvements preprocessing, emphasizing its impact accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, K-means, confirm competitiveness PSO-HE approach, especially The also underscores positive influence clarity precision. These findings highlight promise approach advancing accuracy reliability paving way further method integration to enhance this critical healthcare application.

Язык: Английский

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Computer Vision to Enhance Healthcare Domain: An Overview of Features, Implementation, and Opportunities

Intelligent Pharmacy, Год журнала: 2024, Номер 2(6), С. 792 - 803

Опубликована: Май 21, 2024

The emergence of Artificial Intelligence (AI) has already brought several advantages to the healthcare sector. Computer Vision (CV) is one growing modern AI technologies. distribution and administration medications are about change by using CV for medication management. This system scans pharmaceutical labels keeps track process from delivery cameras, sensors, computer algorithms. In order assure accuracy in medicine dose, also makes it easier doctors, nurses, chemists communicate. vision-driven management can significantly lower number medical mistakes that result inaccurate or missing prescriptions, improper doses, simply forgetting take a particular drug. An exhaustive literature review been done identify work related research objectives. paper their need healthcare. Various tasks associated with domain discussed. Targeted goals through traits briefed. Finally, significant applications CVs were identified Nowadays, practical uses Its methods widely used since they have shown excellent utility contexts, including imaging surgical planning. study how program computers comprehend digital pictures. Numerous utilise this technology, such as automated abnormality identification, illness diagnosis, procedure guiding. expanding quickly enormous promise enhance Some many sector include patient identification systems, picture analysis, simulation diagnosis.

Язык: Английский

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A Survey on Deep Learning in COVID-19 Diagnosis

Journal of Imaging, Год журнала: 2022, Номер 9(1), С. 1 - 1

Опубликована: Дек. 20, 2022

According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases COVID-19, including 65,622,281 deaths worldwide. The spread and severity COVID-19 are alarming. economy life countries worldwide greatly affected. rapid accurate diagnosis directly affects virus degree harm. Currently, classification chest X-ray or CT images based on artificial intelligence is an important method for diagnosis. It can assist doctors in making judgments reduce misdiagnosis rate. convolutional neural network (CNN) very popular computer vision applications, such applied biological image segmentation, traffic sign recognition, face other fields. one most widely used machine learning methods. This paper mainly introduces latest deep methods techniques diagnosing using network. reviews technology CNN at various stages, rectified linear units, batch normalization, data augmentation, dropout, so on. Several well-performing architectures explained detail, AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed discussed existing automatic systems from sensitivity, accuracy, precision, specificity, F1 score. use datasets. Overall, has essential value All them good performance experiments. If expanding datasets, adding GPU acceleration preprocessing techniques, types medical images, will be further improved. wishes make contributions future research.

Язык: Английский

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A comprehensive survey of deep learning algorithms and applications in dental radiograph analysis

Healthcare Analytics, Год журнала: 2023, Номер 4, С. 100282 - 100282

Опубликована: Ноя. 14, 2023

The Integration of machine learning and traditional image processing in dentistry has resulted many applications like automatic teeth identification numbering, caries, anomaly, disease detection, dental treatment prediction. They have a broad scope different observed the literature review. This study reviews on deep radiograph analysis. We present an overview algorithms areas dentistry: tooth Dental predictive models. methods under each area are briefly discussed. data set required for performing experiments is summarized from available literature. concludes by discussing new research opportunities initiatives this field. paper offers comprehensive innovative, challenging, growing dentistry.

Язык: Английский

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Intraoperative in vivo confocal endomicroscopy of the glioma margin: performance assessment of image interpretation by neurosurgeon users

Frontiers in Oncology, Год журнала: 2024, Номер 14

Опубликована: Май 22, 2024

Objectives Confocal laser endomicroscopy (CLE) is an intraoperative real-time cellular resolution imaging technology that images brain tumor histoarchitecture. Previously, we demonstrated CLE may be interpreted by neuropathologists to determine the presence of infiltration at glioma margins. In this study, assessed neurosurgeons’ ability interpret from margins and compared their assessments those neuropathologists. Methods vivo acquired were previously reviewed CLE-experienced four neurosurgeons. A numerical scoring system 0 5 a dichotomous based on pathological features used. Scores hematoxylin eosin (H&E)-stained sections previous study used for comparison. Neurosurgeons’ scores H&E findings. The inter-rater agreement diagnostic performance calculated. concordance between was determined. Results all, 4275 56 margin regions interest (ROIs) included in analysis. With system, neurosurgeons interpreting moderate all ROIs (mean agreement, 61%), which significantly better than 48%) ( p < 0.01). using 83%. systems 93%. overall sensitivity, specificity, positive predictive value, negative value 78%, 32%, 62%, 50%, respectively, 80%, 27%, 61%, 48%, system. No statistically significant differences found Conclusion comparable These results suggest could as guidance tool with or without assistance robust yet simple streamline rapid, simultaneous interpretation during imaging.

Язык: Английский

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A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

IEEE Access, Год журнала: 2023, Номер 11, С. 107309 - 107330

Опубликована: Янв. 1, 2023

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with potential to transform various sectors. BC can offer decentralized secure data storage, while CV allows machines learn understand visual data. The integration of technologies holds massive promise for developing innovative applications that provide solutions challenges in sectors such as supply chain management, healthcare, smart cities, defense. This review explores a comprehensive analysis by examining their combination applications. It also provides detailed fundamental concepts both technologies, highlighting strengths limitations. paper current research efforts make use benefits offered this combination. be used an added layer security systems ensure integrity, enabling image video analytics. open issues associated identified, appropriate future directions proposed.

Язык: Английский

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A survey on comparative study of lung nodules applying machine learning and deep learning techniques

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Авг. 20, 2024

Язык: Английский

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Employing the Artificial Intelligence Object Detection Tool YOLOv8 for Real-Time Pain Detection: A Feasibility Study

Journal of Pain Research, Год журнала: 2024, Номер Volume 17, С. 3681 - 3696

Опубликована: Ноя. 1, 2024

Effective pain management is crucial for patient care, impacting comfort, recovery, and overall well-being. Traditional subjective assessment methods can be challenging, particularly in specific populations. This research explores an alternative approach using computer vision (CV) to detect through facial expressions.

Язык: Английский

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