Search

This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images. The results demonstrate the efficiency of the proposed artificial intelligence model, with a Dice coefficient of 87% and an average Intersection over Union of 0.8, reflecting high agreement with the manual segmentations produced by experts and surpassing traditional interpretation methods. The internal and external validation of the model corroborates its future applicability in real clinical scenarios, offering a new perspective in the analysis of myocardial perfusion scintigraphy images. The integration of artificial intelligence into the process of analyzing myocardial perfusion scintigraphy images represents a significant advancement in diagnostic accuracy, promoting substantial improvements in the interpretation of medical images, and establishing a foundation for future research and clinical applications, such as artifact correction. [ABSTRACT FROM AUTHOR]

This paper outlines the protocol for the deployment of a cloud-based universal medical image repository system. The proposal aims not only at the deployment but also at the automatic expansion of the platform, incorporating Artificial Intelligence (AI) for the analysis of medical image examinations. The methodology encompasses efficient data management through a universal database, along with the deployment of various AI models designed to assist in diagnostic decision-making. By presenting this protocol, the goal is to overcome technical challenges and issues that impact all phases of the workflow, from data management to the deployment of AI models in the healthcare sector. These challenges include ethical considerations, compliance with legal regulations, establishing user trust, and ensuring data security. The system has been deployed, with a tested and validated proof of concept, possessing the capability to receive thousands of images daily and to sustain the ongoing deployment of new AI models to expedite the analysis process in medical image exams. [ABSTRACT FROM AUTHOR]

This paper presents an artificial intelligence-based classification model for the detection of pulmonary embolism in computed tomography angiography. The proposed model, developed from public data and validated on a large dataset from a tertiary hospital, uses a two-dimensional approach that integrates temporal series to classify each slice of the examination and make predictions at both slice and examination levels. The training process consists of two stages: first using a convolutional neural network InceptionResNet V2 and then a recurrent neural network long short-term memory model. This approach achieved an accuracy of 93% at the slice level and 77% at the examination level. External validation using a hospital dataset resulted in a precision of 86% for positive pulmonary embolism cases and 69% for negative pulmonary embolism cases. Notably, the model excels in excluding pulmonary embolism, achieving a precision of 73% and a recall of 82%, emphasizing its clinical value in reducing unnecessary interventions. In addition, the diverse demographic distribution in the validation dataset strengthens the model's generalizability. Overall, this model offers promising potential for accurate detection and exclusion of pulmonary embolism, potentially streamlining diagnosis and improving patient outcomes. [ABSTRACT FROM AUTHOR]

We aim to characterize patients with Gomez‐López‐Hernández syndrome (GLHS) clinically and to investigate them molecularly. A clinical protocol, including a morphological and neuropsychological assessment, was applied to 13 patients with GLHS. Single‐nucleotide polymorphism (SNP) array and whole‐exome sequencing were undertaken; magnetic resonance imaging was performed in 12 patients, including high‐resolution, heavily T2‐weighted sequences (HRT2) in 6 patients to analyze the trigeminal nerves. All patients presented alopecia; two did not present rhombencephalosynapsis (RES); trigeminal anesthesia was present in 5 of the 11 patients (45.4%); brachycephaly/brachyturricephaly and mid‐face retrusion were found in 84.6 and 92.3% of the patients, respectively. One patient had intellectual disability. HRT2 sequences showed trigeminal nerve hypoplasia in four of the six patients; all four had clinical signs of trigeminal anesthesia. No common candidate gene was found to explain GLHS phenotype. RES does not seem to be an obligatory finding in respect of GLHS diagnosis. We propose that a diagnosis of GLHS should be considered in patients with at least two of the following criteria: focal non‐scarring alopecia, rhombencephalosynapsis, craniofacial anomalies (brachyturrycephaly, brachycephaly or mid‐face retrusion), trigeminal anesthesia or anatomic abnormalities of the trigeminal nerve. Studies focusing on germline whole genome sequencing or DNA and/or RNA sequencing of the alopecia tissue may be the next step for the better understanding of GLHS etiology. [ABSTRACT FROM AUTHOR]

Background: Traditional health care systems face long-standing challenges, including patient diversity, geographical disparities, and financial constraints. The emergence of artificial intelligence (AI) in health care offers solutions to these challenges. AI, a multidisciplinary field, enhances clinical decision-making. However, imbalanced AI models may enhance health disparities., Objective: This systematic review aims to investigate the economic performance and equity impact of AI in diagnostic imaging for skin, neurological, and pulmonary diseases. The research question is "To what extent does the use of AI in imaging exams for diagnosing skin, neurological, and pulmonary diseases result in improved economic outcomes, and does it promote equity in health care systems?", Methods: The study is a systematic review of economic and equity evaluations following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and CHEERS (Consolidated Health Economic Evaluation Reporting Standards) guidelines. Eligibility criteria include articles reporting on economic evaluations or equity considerations related to AI-based diagnostic imaging for specified diseases. Data will be collected from PubMed, Embase, Scopus, Web of Science, and reference lists. Data quality and transferability will be assessed according to CHEC (Consensus on Health Economic Criteria), EPHPP (Effective Public Health Practice Project), and Welte checklists., Results: This systematic review began in March 2023. The literature search identified 9,526 publications and, after full-text screening, 9 publications were included in the study. We plan to submit a manuscript to a peer-reviewed journal once it is finalized, with an expected completion date in January 2024., Conclusions: AI in diagnostic imaging offers potential benefits but also raises concerns about equity and economic impact. Bias in algorithms and disparities in access may hinder equitable outcomes. Evaluating the economic viability of AI applications is essential for resource allocation and affordability. Policy makers and health care stakeholders can benefit from this review's insights to make informed decisions. Limitations, including study variability and publication bias, will be considered in the analysis. This systematic review will provide valuable insights into the economic and equity implications of AI in diagnostic imaging. It aims to inform evidence-based decision-making and contribute to more efficient and equitable health care systems., International Registered Report Identifier (irrid): DERR1-10.2196/48544., (©Giulia Osório Santana, Rodrigo de Macedo Couto, Rafael Maffei Loureiro, Brunna Carolinne Rocha Silva Furriel, Edna Terezinha Rother, Joselisa Péres Queiroz de Paiva, Lucas Reis Correia. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 28.12.2023.)

Background More than four million people today live with Hansen's disease, and 200,000 new cases are diagnosed every year. Lifetime effects of Hansen's disease manifest as changes to bones of the face, hands and feet, resulting in physical impairment, secondary complications and facial changes that can be detrimental to quality of life, particularly among the elderly. Aims This study aimed to perform a detailed characterization of rhinomaxillary syndrome and its clinical manifestations in older persons treated in the past for Hansen's disease. Methods This was a cross-sectional study to characterize rhinomaxillary syndrome among older persons (age 60+ years) resident at Pedro Fontes Hospital, Cariacica, Espírito Santo, Brazil. Computed tomography images were examined with three-dimensional reconstructions to assess alterations to maxillofacial bones according to criteria for radiological rhinomaxillary syndrome. Participants were examined to assess facial alterations according to criteria for clinical rhinomaxillary syndrome. Results Rhinomaxillary syndrome was investigated in 16 participants (ten females and six males), median age 70 (range 60-89) years, age at diagnosis 20 (6-43) years and time since diagnosis 46 (26-70) years. Four participants fully met radiological rhinomaxillary syndrome criteria, four partially. All participants with full radiological rhinomaxillary syndrome presented with facial changes which met criteria for clinical rhinomaxillary syndrome, including "saddle nose" (loss of nasal dorsal height and shortened length of nose, due to cartilaginous and/or bone collapse), concave middle third of the face with sunken nose, maxillary retrognathia and inverted upper lip. Limitations Clinical histories were incomplete for some participants because records were lost at the hospital over time. Conclusion Until Hansen's disease is eliminated from endemic countries, persons affected will continue to present with rhinomaxillofacial alterations caused by Mycobacterium leprae infection. Clinical protocols for assessment and long-term care need to include otorhinolaryngological evaluation, mainly to prevent secondary complications. When rhinomaxillofacial bone changes are suspected, this evaluation should be supported by computed tomography imaging, if available.