Manual diagnosis

Timely diagnosis of mild traumatic brain injury (mTBI) remains challenging due to the rapid recovery of acute symptoms and the absence of evidence of injury in static neuroimaging scans. Furthermore, while longitudinal tracking of mTBI is essential in understanding how the diseases progresses/regresses over time for enhancing personalized patient care, a standardized approach for this purpose is not yet available. Recent functional neuroimaging studies have provided evidence of brain function alterations following mTBI, suggesting mTBI-detection models can be built based on these changes...

SUMMARY: We found LLM, traditional machine learning, and deep learning had diverse error profiles on cognitive decline identification from clinical notes, and the ensemble of LLM, machine learning, and deep learning achieved state of the art performance. BACKGROUND: Early detection of cognitive decline in elderly individuals facilitates clinical trial enrollment and timely medical interventions. This study aims to apply, evaluate, and compare advanced natural language processing techniques for identifying signs of cognitive decline in clinical notes...

OBJECTIVES: To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4. MATERIALS AND METHODS: This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign)...

Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning...

BACKGROUND: Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease's spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission...

Accurate segmentation of coronary artery tree and personalised 3D printing from medical images is essential for CAD diagnosis and treatment. The current literature on 3D printing relies solely on generic models created with different software or 3D coronary artery models manually segmented from medical images. Moreover, there are not many studies examining the bioprintability of a 3D model generated by artificial intelligence (AI) segmentation for complex and branched structures. In this study, deep learning algorithms with transfer learning have been employed for accurate segmentation of the coronary artery tree from medical images to generate printable segmentations...

Large-scale digital whole slide image (WSI) datasets analysis have gained significant attention in computer-aided cancer diagnosis. Content-based histopathological image retrieval (CBHIR) is a technique that searches a large database for data samples matching input objects in both details and semantics, offering relevant diagnostic information to pathologists. However, the current methods are limited by the difficulty of gigapixels, the variable size of WSIs, and the dependence on manual annotations. In this work, we propose a novel histopathology language-image representation learning framework for fine-grained digital pathology cross-modal retrieval, which utilizes paired diagnosis reports to learn fine-grained semantics from the WSI...

Helicobacter pylori (H. pylori) infection correlates closely with gastric diseases such as gastritis, ulcers, and cancer, influencing more than half of the world's population. Establishing a rapid, precise, and automated platform for H. pylori diagnosis is an urgent clinical need and would significantly benefit therapeutic intervention. Recombinase polymerase amplification (RPA)-CRISPR recently emerged as a promising molecular diagnostic assay due to its rapid detection capability, high specificity, and mild reaction conditions...

Early detection and treatment of breast cancer can significantly reduce patient mortality, and mammogram is an effective method for early screening. Computer-aided diagnosis (CAD) of mammography based on deep learning can assist radiologists in making more objective and accurate judgments. However, existing methods often depend on datasets with manual segmentation annotations. In addition, due to the large image sizes and small lesion proportions, many methods that do not use region of interest (ROI) mostly rely on multi-scale and multi-feature fusion models...

BACKGROUND: Reported drug allergies are commonly encountered by surgeons and can lead to uncertainty in selecting an appropriate agent due to concerns of associated risks with related and cross-reactive drugs. This uncertainty can ultimately lead to increased infection rates. METHODS: A literature review was conducted in PubMed using a combination of the terms "allergy," "allergic reaction," "anaphylaxis," and "surgery," "surgical," or "operating room" for articles published within the last 10 years...

BACKGROUND: Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction. METHODS: A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines...

Gaudiani et al. (2022) presented terminal anorexia nervosa (T-AN) as a potential new specifier to the anorexia nervosa (AN) diagnosis, with criteria including (a) AN diagnosis, (b) age > 30 years, (c) previously participated in high-quality care, and (d) the clear, consistent determination by a patient with decision-making capacity that additional treatment would be futile, knowing death will result. This study's purpose was to empirically examine a subgroup of participants with AN who met the first three criteria of T-AN-and a smaller subset who also met a proxy index of the fourth criterion involving death (TD-AN)-and compare them to an adult "not terminal" anorexia nervosa (NT-AN) group and to a "not terminal" subset 30 years of age or older (NTO-AN)...

PURPOSE: The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos. METHODS: A systematic review of the literature about intra-operative analysis of cataract surgery videos with machine learning techniques was performed. Cataract diagnosis and detection algorithms were excluded. Resulting algorithms were compared, descriptively analyzed, and metrics summarized or visually reported...

OBJECTIVES: Attention Deficit Hyperactivity Disorder (ADHD) and obesity are major pediatric public health problems. The present study aimed to examine the association between these two health parties in our pediatric populations. MATERIALS & METHODS: This study is a single group retrospective cohort study about Body Mass Index (BMI) changes in 149 children and adolescents between 3-18 years old with a diagnosis of ADHD based on one child and adolescent psychiatrist interview according to the Diagnostic and Statistical Manual of Mental Disorders 4th edition criteria (DSM-IV-TR)...

INTRODUCTION: This study examined the long-term effectiveness of cognitive behavioral therapy (CBT) (≥ 2 years after the end of therapy) in the routine care of youth (mean 11.95 years; SD = 3.04 years) with primary anxiety disorder (AD). METHODS: Two hundred and ten children with any AD as a primary diagnosis and with any comorbidity were included in the "Kids Beating Anxiety (KibA)" clinical trial and received evidence-based CBT. Diagnoses, severity of diagnoses, and further dimensional outcome variables of symptoms and functioning were assessed before (baseline), after the last treatment session (POST), and at two follow-up (FU) assessments in the child and caregiver report: 6 months (6MONTHS-FU) and >2 years (mean 4...

OBJECTIVES: Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system...

INTRODUCTION: Natural Language Processing (NLP)-based data extraction from electronic health records (EHRs) holds significant potential to simplify clinical management and aid research. This review aims to evaluate the current landscape of NLP-based data extraction in prostate cancer (PCa) management. MATERIALS AND METHODS: We conducted a literature search of PubMed and Google Scholar databases using the keywords: "Natural Language Processing," "Prostate Cancer," "data extraction," and "EHR...

AIMS: To provide real-world data on post-diuretic spot urine sodium concentration (UNa) assessment in acute heart failure (AHF) and its implications for treatment. METHODS AND RESULTS: Automated query of the electronic medical record identified patients admitted to the cardiac intensive care unit of a single tertiary care hospital between November 2018 and December 2021, who received intravenous loop diuretics. Detailed manual chart review confirmed the AHF diagnosis...

The present study aimed to monitor the practices of European veterinarians for the diagnosis and treatment of retained fetal membranes in cattle. A questionnaire was established and distributed to veterinarians from five European countries. A total of 700 veterinarians participated in the survey. A vaginal examination, general examination and uterine palpation are carried out by 71%, 38% and 23% of veterinarians, respectively. Moreover, half of the veterinarians attempt to remove the placenta manually, 70% of them administer a combined local and general treatment if the cow has a fever (more than 39...

Post mortem hyoid bone fracture findings may be attributable to various factors, including both the onset of acute mechanical asphyxia as it happens in manual strangulation and in charred corpses. In forensic practice, the discovery of corpses burned after death to hide their real cause of death is not uncommon: in these cases, the diagnostic challenge is even greater, as the action of flames is capable of both masking previously generated lesions and/or generating new ones, as occurs for hyoid bone fractures...