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Use of artificial intelligence in the diagnosis of alterations in cervical cytology: A university population-based observational study.

INTRODUCTION: Conventional cervical cytology (Pap smear) remains a primary method for cervical cancer screening in Colombia, despite limitations in diagnostic yield and heavy workload. The potential of artificial intelligence to address these challenges is yet to be evaluated in our population. OBJECTIVE: To evaluate and compare the discriminative ability of four artificial intelligence-based models for the detection of abnormalities in Pap smears. Materials and methods. A total of 650 images of Pap smear cells were obtained from a university cohort in northeastern Colombia. These images were…

Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives.

Artificial intelligence (AI) is rapidly transforming diagnostic approaches in different fields of medical sciences, demonstrating an emerging potential to revolutionize dermatopathology due to its capacity to process large amounts of data in the shortest possible time, both for diagnosis and research purposes. Different AI models have been applied to neoplastic skin diseases, especially melanoma. However, to date, very few studies have investigated the role of AI in dermatoses. Herein, we provide an overview of the key aspects of AI and its functioning, focusing on medical applications. Then, …

Artificial intelligence for prediction of atrial fibrillation in the stroke unit: a retrospective derivation validation cohort study.

BACKGROUND: Paroxysmal atrial fibrillation (AF) is a major cause of stroke but is often undetected in routine clinical practice. Effective stratification is needed to identify patients with stroke who might benefit the most from intensified AF screening. Several artificial intelligence models have been proposed to predict AF based on ECG in sinus rhythm, but broad implementation has been limited. The most valuable input features and most effective model design for AF prediction are also unclear. METHODS: We developed and tested AF prediction models utilising continuous electrocardiogram monit…

Leveraging In Silico and Artificial Intelligence Models to Advance Drug Disposition and Response Predictions Across the Lifespan.

Incorporating inter-individual differences in drug disposition and responses is essential for ensuring the safe and effective use of drugs i … data (RWD) to predict drug exposure, efficacy, and safety. Additionally, machine learning (ML) and artificial intelligence (AI) offer powerful tools for analyzing large datasets and identifying key physiological determinants of drug response across the lifespan. This review discusses the application of in silico and AI models to advance the prediction of drug exposure a …

Evaluating the Diagnostic Accuracy of an Artificial Intelligence Tool for Pulmonary Abnormalities in Chest Radiographs: A Retrospective Study.

Introduction A retrospective, in-clinic study was conducted to validate the diagnostic performance of the Oxipit ChestEye™ AI tool in interpreting chest radiographs and to evaluate its impact on clinical outcomes and healthcare system efficiency. The performance of the AI model was compared with that of radiologists. Methods Chest radiographs from 1,470 patients, obtained from a public healthcare institution, were analyzed for nine pulmonary conditions: consolidation, heart enlargement, interstitial markers, linear atelectasis, no cardiopulmonary findings, nodule, pleural effusion, pneumothora

An artificial intelligence-based diagnosis system for the identification of helminth parasitic infections in mithun and allied bovines.

This study presents a novel deep learning approach addressing the critical shortage of veterinary expertise in India’s North Eastern Hill (NEH) region through automated identification of parasitic infections in livestock. We developed a Convolutional Neural Network (CNN) architecture capable of analyzing both standard and microscopic images to identify and classify 16 distinct parasitic species. The model comprises four convolutional layers (32, 64, 128, 256 filters) with ReLU activation and MaxPooling for efficient feature extraction, followed by Dense layers and a Softmax classifier. The mod

Интеграция больших данных и искусственного интеллекта для прогнозирования прогресса в множестве склерозов: проблемы и путь вперед.

Множественный склероз (MS) по-прежнему является сложным и дорогостоящим неврологическим заболеванием, характеризующимся постепенной инвалидностью, что делает необходимым раннее обнаружение и точное прогнозирование прогрессирующего заболевания. В то время как искусственный интеллект (АИ) в сочетании с большими данными обещает преобразующие достижения в индивидуализированном уходе за НР, интеграция мультимодальных, реальных массивов данных, включая клинические записи, магнитно-резонансную съемку (MRI) и цифровые биомаркеры, остается ограниченной. В настоящем перспективном документе выявляется кр

Совпадение в аннотации оригинала
Multiple sclerosis (MS) remains a complex and costly neurological condition characterised by progressive disability, making early detection and accurate prognosis of disease progression imperative. While artificial intelligence (AI) combined with big data promises transformative advances in personalised MS care, integration of multimodal, real-world datasets…

Clinical evaluation of the efficacy of limbus artificial intelligence software to augment contouring for prostate and nodes radiotherapy.

OBJECTIVES: To determine if Limbus, an artificial intelligence (AI) auto-contouring software, can offer meaningful time savings for prostate radiotherapy treatment planning. METHODS: Three clinical oncologists recorded the time taken to contour prostate and seminal vesicles, lymph nodes, bladder, rectum, bowel, and femoral heads on CT scans for 30 prostate patients (15 prostate, 15 prostate and nodes). Limbus 1.6.0 was used to generate these contours on the 30 CT scans. The time taken by the oncologists to modify individual Limbus contours was noted and compared with manual contouring times. …

Diagnostic accuracy of artificial intelligence for approximal caries on bitewing radiographs: A systematic review and meta-analysis.

OBJECTIVES: This systematic review and meta-analysis aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) for approximal carious lesions on bitewing radiographs. METHODS: This study included randomized controlled trials (RCTs) and non-randomized controlled trials (non-RCTs) reporting on the diagnostic accuracy of AI for approximal carious lesions on bitewing radiographs. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A systematic search was conducted on November 4, 2023, in PubMed, Cochrane, and Embase databas…

Current status of artificial intelligence analysis for the diagnosis of gallbladder diseases using ultrasonography: a scoping review.

BACKGROUND: Ultrasound (US) is the first-line imaging method for gallbladder diseases (GBDs), with advantages of easy accessibility, real-time dynamic imaging, and no radiation. However, using only visual judgment from US images to stratify the risk of gallbladder (GB) lesions is challenging. In addition, the diagnostic ability of sonographers is highly correlated with their knowledge reserves, clinical experience, and proficiency in operation. Recently, the application of artificial intelligence (AI) in medical image recognition has attracted widespread attention. This review aims to provide …

Use of Artificial Intelligence With Deep Learning Approaches for the Follow-up of Infrarenal Endovascular Aortic Repair.

INTRODUCTION: Endoleaks represent one of the main complications after endovascular aortic repair (EVAR) and can lead to increased re-intervention rates and secondary rupture. Serial lifelong surveillance is required and traditionally involves cross-sectional imaging with manual axial measurements. Artificial intelligence (AI)-based imaging analysis has been developed and may provide a more precise and faster assessment. This study aims to evaluate the ability of an AI-based software to assess post-EVAR morphological changes over time, detect endoleaks, and associate them with EVAR-related adve…

Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis.

Pneumonia is declared a global emergency public health crisis in children less than five age and the geriatric population. Recent advancements in deep learning models could be utilized effectively for the timely and early diagnosis of pneumonia in immune-compromised patients to avoid complications. This systematic review and meta-analysis utilized PRISMA guidelines for the selection of ten articles included in this study. The literature search was done through electronic databases including PubMed, Scopus, and Google Scholar from 1st January 2016 till 1 July 2023. Overall studies included a to

Advancements and turning point of artificial intelligence in ophthalmology: A comprehensive analysis of research trends and collaborative networks.

Artificial intelligence (AI) has emerged as a transformative force with great potential in various fields, including healthcare. In recent years, AI has garnered significant attention due to its potential to revolutionise ophthalmology, leading to advancements in patient care such as disease detection, diagnosis, treatment and monitoring of disease progression. This study presents a comprehensive analysis of the research trends and collaborative networks at the intersection of AI and ophthalmology. In this study, we conducted an extensive search of the Web of Science Core Collection to identif…

Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation.

OBJECTIVE: This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods. METHODS: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predi…

[Application of artificial intelligence-based organ contouring software and comparison of results in treatment planning for radiotherapy].

Introduction: The first step in the radiotherapy treatment of cancer patients is the treatment planning, which involves delineations of the … rocess more accurate and consistent. Objective: To compare the quality of contouring software using artificial intelligence-based algorithms, and to define the organs that can be outlined with the best consistency, and those organs where there are large differences in segmentation between different software. Method: Contours of organs at risk defined by three contouring …

Use of artificial intelligence and I-Score for prediction of recurrence before catheter ablation of atrial fibrillation.

BACKGROUND: Based solely on pre-ablation characteristics, previous risk scores have demonstrated variable predictive performance. This study aimed to predict the recurrence of AF after catheter ablation by using artificial intelligence (AI)-enabled pre-ablation computed tomography (PVCT) images and pre-ablation clinical data. METHODS: A total of 638 drug-refractory paroxysmal atrial fibrillation (AF) patients undergone ablation were recruited. For model training, we used left atria (LA) acquired from pre-ablation PVCT slices (126,288 images). A total of 29 clinical variables were collected be…

An artificial intelligence algorithm for co-clustering to help in pharmacovigilance before and during the COVID-19 pandemic.

AIMS: Monitoring drug safety in real-world settings is the primary aim of pharmacovigilance. Frequent adverse drug reactions (ADRs) are usually identified during drug development. Rare ones are mostly characterized through post-marketing scrutiny, increasingly with the use of data mining and disproportionality approaches, which lead to new drug safety signals. Nonetheless, waves of excessive numbers of reports, often stirred up by social media, may overwhelm and distort this process, as observed recently with levothyroxine or COVID-19 vaccines. As human resources become rarer in the field of p

[Exploration of the clinical transformation application of the artificial intelligence diagnosis system for bladder cancer based on cystoscopy].

This study aims to explore the possibility and bottleneck of clinical translation for an artificial intelligence (AI) diagnosis system for bladder cancer based on cystoscopy.We retrospectively collected videos of 101 bladder cancer patients from January to November 2023, at Sun Yat-sen Memorial Hospital, Sun Yat-sen University. Among these patients, with a median age of 63 years and 81.0% were male. The bladder cancer AI diagnosis system was utilized for diagnosis, and the accuracy of diagnoses from the videos was assessed. Additionally, a surgical evaluation scale was formulated to evaluate t…

Artificial intelligence-based video monitoring of movement disorders in the elderly: a review on current and future landscapes.

Due to global ageing, the burden of chronic movement and neurological disorders (Parkinson's disease and essential tremor) is rapidly increasing. Current diagnosis and monitoring of these disorders rely largely on face-to-face assessments utilising clinical rating scales, which are semi-subjective and time-consuming. To address these challenges, the utilisation of artificial intelligence (AI) has emerged. This review explores the advantages and challenges associated with using AI-driven video monitoring to care for elderly patients with movement disorders. The AI-based video monitoring systems…