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An Artificial Intelligence Olfactory-Based Diagnostic Model for Parkinson's Disease Using Volatile Organic Compounds from Ear Canal Secretions.

Parkinson's Disease (PD), a frequently diagnosed neurodegenerative condition, poses a major global challenge. Early diagnosis and intervention are crucial for PD treatment. This study proposes a diagnostic model for PD that analyzes volatile organic compounds (VOCs) from ear canal secretions (ECS). Using gas chromatography-mass spectrometry (GC-MS) to examine ECS samples from patients, four VOC components (ethylbenzene, 4-ethyltoluene, pentanal, and 2-pentadecyl-1,3-dioxolane) were identified as biomarkers with statistically significant differences between PD and non-PD patients. Diagnostic mo

Impact of Artificial Intelligence on the Timing of Recurrent Laryngeal Nerve Recognition during Robot-Assisted Minimally Invasive Esophagectomy.

BACKGROUND: As a first step to prevent recurrent laryngeal nerve (RLN) palsy, we have developed an artificial intelligence (AI)-based anatomical recognition system for critical anatomical structures in robot-assisted minimally invasive esophagectomy (RAMIE). In the present study, we investigated whether AI would enable surgeons to rapidly recognize the RLN. PATIENTS AND METHODS: Five surgical videos of RAMIE were used to validate the AI. The confidence level (CL) was established as a new criterion to define how confidently surgeons recognize the RLN and stratified into three levels: CL0 is th…

Artificial Intelligence Supported Analysis of Anal Sphincter and Levator Ani Muscle Using Medical Imaging Techniques: A Systematic Review.

INTRODUCTION AND HYPOTHESIS: Injuries to the anal sphincter (AS) and levator ani muscles (LAM) are linked to pelvic floor disorders and can be assessed through medical imaging. Artificial intelligence (AI), particularly machine learning and deep learning, is increasingly used in pelvic floor imaging to support clinicians in their diagnostic assessment. However, the quality of AI research in this domain has not been systematically evaluated. Reviewing current AI research and its applications is essential to understand its benefits, as well as its limitations and knowledge gaps. METHODS: We con…

Artificial intelligence-based incisive canal visualization for preventing and detecting post-implant injury, using cone beam computed tomography.

The aim of this study was to clinically validate an artificial intelligence (AI)-based tool for automatic segmentation of the mandibular incisive canal (MIC) on cone beam computed tomography (CBCT), enabling prevention and detection of iatrogenic implant-related nerve injuries. Patient records from University Hospitals Leuven were screened for CBCT related to implant surgery cases with nerve injuries. CBCT scans were imported into Virtual Patient Creator for canal segmentation and 3D model generation. Two oral radiologists compared the AI-segmented canals with respective CBCT images. Five obse…

Artificial Intelligence Models as Predictors of Poor Outcomes in Transsphenoidal Pituitary Tumor Surgeries: A Diagnostic Test Accuracy Review.

Pituitary tumors are the second most common type of brain tumor in adults, accounting for 17% of adult tumors. Over the years, endoscopic transsphenoidal surgery has become widely used in the treatment of pituitary tumors. With the recent advancement of Artificial Intelligence (AI) and Machine Learning (ML) technologies to generate clinical predictions that aid in diagnostic medicine, they have stood out as preoperative planning tools with the aim of predicting clinical outcomes, including postoperative ones. This article aims to evaluate the applicability of ML models in predicting postoperat…

Artificial intelligence-based radiogenomics reveals the potential immunoregulatory role of COL22A1 in glioma and its induced autoimmune encephalitis.

BACKGROUND: The tumor microenvironment plays a crucial role in the progression of both glioma and glioma-induced autoimmune encephalitis. However, there remains a significant lack of effective therapeutic targets for these diseases. METHOD: We collected 54 CT images of glioma patients and 54 glioma-induced autoimmune encephalitis patients, respectively. Radiomics features were extracted from tumors and encephalitis regions using Python, followed by dimensionality reduction via random forest and lasso regression, and construction of radiomics-based risk scores. Genomic data matched with clinic

Mortality Prediction from Patient's First Day PAAC Radiograph in Internal Medicine Intensive Care Unit Using Artificial Intelligence Methods.

Introduction: This study aims to predict mortality using chest radiographs obtained on the first day of intensive care admission, thereby contributing to better planning of doctors' treatment strategies and more efficient use of limited resources through early and accurate predictions. Methods: We retrospectively analyzed 510 ICU patients. After data augmentation, a total of 3019 chest radiographs were used for model training and validation, while an independent, non-augmented test set of 100 patients (100 images) was reserved for final evaluation. Seventy-four (74) radiomic features were extr

Artificial intelligence-assisted detection of nasopharyngeal carcinoma on endoscopic images: a national, multicentre, model development and validation study.

BACKGROUND: Nasopharyngeal carcinoma is highly curable when diagnosed early. However, the nasopharynx's obscure anatomical position and the … 89·3-93·5), and a specificity of 86·1% (95% CI 84·1-87·9). In the multireader, multicase study, the artificial intelligence (AI)-assisted strategy enhanced otolaryngologists' diagnostic accuracy by 7·9%, increasing from 83·4% (95% CI 80·1-86·7, without AI assistance) to 91·2% (95% CI 88·6-93·9, with AI assistance; p<0·0001) for primary care otolaryngologists. Reading tim …

Dual-stage artificial intelligence-powered screening for accurate classification of thyroid nodules: enhancing fine needle aspiration biopsy precision.

BACKGROUND: Accurately differentiating thyroid conditions, particularly malignant and benign nodules, is crucial for effective treatment planning. Fine needle aspiration biopsy (FNAB), the standard diagnostic method for suspected malignancies detected by ultrasound, is invasive and can be unnecessary. This study introduces a novel dual-stage deep learning architecture for four-class classification of thyroid nodules, aiming to improve diagnostic accuracy and reduce unnecessary procedures. The framework integrates segmentation and classification stages, offering more precise and consistent anal

Artificial intelligence model for automatic 3-dimensional reconstruction of ossicular chain and bony labyrinth from high-resolution CT.

BACKGROUND: Three-dimensional (3D) reconstruction of ossicular chain and bony labyrinth based on temporal bone high-resolution CT (HRCT) is useful for diagnosis and treatment guidance of middle and inner ear diseases. However, these structures are small and irregular, making manual reconstruction time-consuming. PURPOSE: To develop and validate an artificial intelligence (AI) model based on semisupervised learning for automated 3D reconstruction of ossicular chain and bony labyrinth on HRCT images. METHODS: HRCT images from 304 ears of 152 consecutive patients retrospectively collected from …

Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta-Analysis.

BACKGROUND AND AIMS: Odontogenic keratocyst (OKC) is a radiolucent jaw lesion often mistaken for similar conditions like ameloblastomas on panoramic radiographs. Accurate diagnosis is vital for effective management, but manual image interpretation can be inconsistent. While deep learning algorithms in AI have shown promise in improving diagnostic accuracy for OKCs, their performance across studies is still unclear. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of AI models in detecting OKC from panoramic radiographs. METHODS: A systematic search was perfor

Performance of Chest CT-Based Artificial Intelligence Models in Distinguishing Pulmonary Mucormycosis, Invasive Pulmonary Aspergillosis, and Pulmonary Tuberculosis.

In clinical practice, differentiating among pulmonary mucormycosis (PM), invasive pulmonary aspergillosis (IPA), and pulmonary tuberculosis (PTB) can be challenging. This study aimed to evaluate the performance of chest CT-based artificial intelligence (AI) models in distinguishing among these three diseases. Patients with confirmed PM, IPA, or PTB were retrospectively recruited from three tertiary hospitals. Two models were developed: unanotated supervised training (UST) model trained with original CT images and annotated supervised training (AST) model trained with manually annotated lesion …

Histological Image Classification Between Follicular Lymphoma and Reactive Lymphoid Tissue Using Deep Learning and Explainable Artificial Intelligence (XAI).

Background/Objectives: The major question that confronts a pathologist when evaluating a lymph node biopsy is whether the process is benign or malignant, and the differential diagnosis between follicular lymphoma and reactive lymphoid tissue can be challenging. Methods: This study designed a convolutional neural network based on ResNet architecture to classify a large series of 221 cases, including 177 follicular lymphoma and 44 reactive lymphoid tissue/lymphoid hyperplasia, which were stained with hematoxylin and eosin (H&E). Explainable artificial intelligence (XAI) methods were used for int…

Исфахан Искусственное Разведывательное событие 2023 года: Сегментация и локализация в магнитных резонансных изображениях пациентов с множественным склерозом.

Множественный склероз (MS) является одной из наиболее распространенных причин неврологических расстройств у молодых людей. Болезнь возникает, когда иммунная система атакует центральную нервную систему и разрушает миелин нервных клеток. Это приводит к обнаружению нескольких повреждений в изображениях пациентов с магнитным резонансом (MR). Точное определение количества и места повреждений может помочь врачам определить тяжесть и прогресс болезни. МЕТОД: В силу важности этой проблемы эта задача была посвящена сегментации и локализации повреждений в образе пациентов с MR с MS. Цель заключалась в т

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… In recent years, artificial intelligence (AI) has been increasingly become attractive in the diagnosis of different diseases. AI can increase the precision, quality, and speed of diagnosis based on different medical images. …

Image quality assessment of artificial intelligence iterative reconstruction for low dose unenhanced abdomen: comparison with hybrid iterative reconstruction.

OBJECTIVES: To assess the impact of artificial intelligence iterative reconstruction algorithms (AIIR) on image quality with phantom and clinical studies. METHODS: The phantom images were reconstructed with the hybrid iterative algorithm (HIR: Karl 3D-3, 5, 7, 9) and AIIR (grades 1-5) algorithm. Noise power spectra (NPS), task transfer functions (TTF) were measured, and additionally sharpness was assessed using a "blur metric" procedure. Sixty-two consecutive patients underwent standard-dose and low-dose unenhanced abdominal computed tomography (CT) scans, i.e., SDCT and LDCT groups, respecti…

Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.

In recent years, many neuroimaging studies have applied artificial intelligence (AI) to facilitate existing challenges in Parkinson's disease (PD) diagnosis, prognosis, and intervention. The aim of this systematic review was to provide an overview of neuroimaging-based AI studies and to assess their methodological quality. A PubMed search yielded 810 studies, of which 244 that investigated the utility of neuroimaging-based AI for PD diagnosis, prognosis, or intervention were included. We systematically categorized studies by outcomes and rated them with respect to five minimal quality criteria…

The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease.

BACKGROUND: Large language model (LLM)-linked chatbots may be an efficient source of clinical recommendations for healthcare providers and patients. This study evaluated the performance of LLM-linked chatbots in providing recommendations for the surgical management of gastroesophageal reflux disease (GERD). METHODS: Nine patient cases were created based on key questions addressed by the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) guidelines for the surgical treatment of GERD. ChatGPT-3.5, ChatGPT-4, Copilot, Google Bard, and Perplexity AI were queried on November 16th

The diagnostic efficiency of artificial intelligence based 2 hours Holter monitoring in premature ventricular and supraventricular contractions detection.

BACKGROUND: Electrocardiography (ECG) and 24 hours Holter monitoring (24 h-Holter) provided valuable information for premature ventricular and supraventricular contractions (PVC and PSVC). Currently, artificial intelligence (AI) based 2 hours single-lead Holter (2 h-Holter) monitoring may provide an improved strategy for PSVC/PVC diagnosis. HYPOTHESIS: AI combined with single-lead Holter monitoring improves PSVC/PVC detection. METHODS: In total, 170 patients were enrolled between August 2022 and 2023. All patients wore both devices simultaneously; then, we compared diagnostic efficiency, inc…

Accuracy and safety of an autonomous artificial intelligence clinical assistant conducting telemedicine follow-up assessment for cataract surgery.

BACKGROUND: Artificial intelligence deployed to triage patients post-cataract surgery could help to identify and prioritise individuals who need clinical input and to expand clinical capacity. This study investigated the accuracy and safety of an autonomous telemedicine call (Dora, version R1) in detecting cataract surgery patients who need further management and compared its performance against ophthalmic specialists. METHODS: 225 participants were recruited from two UK public teaching hospitals after routine cataract surgery between 17 September 2021 and 31 January 2022. Eligible patients r…