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BMC Oral Health Jan 2024Since AI algorithms can analyze patient data, medical records, and imaging results to suggest treatment plans and predict outcomes, they have the potential to support... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Since AI algorithms can analyze patient data, medical records, and imaging results to suggest treatment plans and predict outcomes, they have the potential to support pathologists and clinicians in the diagnosis and treatment of oral and maxillofacial pathologies, just like every other area of life in which it is being used. The goal of the current study was to examine all of the trends being investigated in the area of oral and maxillofacial pathology where AI has been possibly involved in helping practitioners.
METHODS
We started by defining the important terms in our investigation's subject matter. Following that, relevant databases like PubMed, Scopus, and Web of Science were searched using keywords and synonyms for each concept, such as "machine learning," "diagnosis," "treatment planning," "image analysis," "predictive modelling," and "patient monitoring." For more papers and sources, Google Scholar was also used.
RESULTS
The majority of the 9 studies that were chosen were on how AI can be utilized to diagnose malignant tumors of the oral cavity. AI was especially helpful in creating prediction models that aided pathologists and clinicians in foreseeing the development of oral and maxillofacial pathology in specific patients. Additionally, predictive models accurately identified patients who have a high risk of developing oral cancer as well as the likelihood of the disease returning after treatment.
CONCLUSIONS
In the field of oral and maxillofacial pathology, AI has the potential to enhance diagnostic precision, personalize care, and ultimately improve patient outcomes. The development and application of AI in healthcare, however, necessitates careful consideration of ethical, legal, and regulatory challenges. Additionally, because AI is still a relatively new technology, caution must be taken when applying it to this industry.
Topics: Humans; Algorithms; Artificial Intelligence; Image Processing, Computer-Assisted; Medical Records; Mouth; Face
PubMed: 38263027
DOI: 10.1186/s12903-023-03533-7 -
JAMA Jan 2024Children with speech and language difficulties are at risk for learning and behavioral problems.
IMPORTANCE
Children with speech and language difficulties are at risk for learning and behavioral problems.
OBJECTIVE
To review the evidence on screening for speech and language delay or disorders in children 5 years or younger to inform the US Preventive Services Task Force.
DATA SOURCES
PubMed/MEDLINE, Cochrane Library, PsycInfo, ERIC, Linguistic and Language Behavior Abstracts (ProQuest), and trial registries through January 17, 2023; surveillance through November 24, 2023.
STUDY SELECTION
English-language studies of screening test accuracy, trials or cohort studies comparing screening vs no screening; randomized clinical trials (RCTs) of interventions.
DATA EXTRACTION AND SYNTHESIS
Dual review of abstracts, full-text articles, study quality, and data extraction; results were narratively summarized.
MAIN OUTCOMES AND MEASURES
Screening test accuracy, speech and language outcomes, school performance, function, quality of life, and harms.
RESULTS
Thirty-eight studies in 41 articles were included (N = 9006). No study evaluated the direct benefits of screening vs no screening. Twenty-one studies (n = 7489) assessed the accuracy of 23 different screening tools that varied with regard to whether they were designed to be completed by parents vs trained examiners, and to screen for global (any) language problems vs specific skills (eg, expressive language). Three studies assessing parent-reported tools for expressive language skills found consistently high sensitivity (range, 88%-93%) and specificity (range, 88%-85%). The accuracy of other screening tools varied widely. Seventeen RCTs (n = 1517) evaluated interventions for speech and language delay or disorders, although none enrolled children identified by routine screening in primary care. Two RCTs evaluating relatively intensive parental group training interventions (11 sessions) found benefit for different measures of expressive language skills, and 1 evaluating a less intensive intervention (6 sessions) found no difference between groups for any outcome. Two RCTs (n = 76) evaluating the Lidcombe Program of Early Stuttering Intervention delivered by speech-language pathologists featuring parent training found a 2.3% to 3.0% lower proportion of syllables stuttered at 9 months compared with the control group when delivered in clinic and via telehealth, respectively. Evidence on other interventions was limited. No RCTs reported on the harms of interventions.
CONCLUSIONS AND RELEVANCE
No studies directly assessed the benefits and harms of screening. Some parent-reported screening tools for expressive language skills had reasonable accuracy for detecting expressive language delay. Group parent training programs for speech delay that provided at least 11 parental training sessions improved expressive language skills, and a stuttering intervention delivered by speech-language pathologists reduced stuttering frequency.
Topics: Child; Humans; Language Development Disorders; Preventive Health Services; Speech; Speech Disorders; Stuttering; Practice Guidelines as Topic; Infant; Child, Preschool; Mass Screening
PubMed: 38261038
DOI: 10.1001/jama.2023.24647 -
The Medico-legal Journal Dec 2023A judicial on-site examination is essential for the correct analysis of a forensic case, particularly when there has been a fatal fire, as heat-related changes to bodies... (Review)
Review
A judicial on-site examination is essential for the correct analysis of a forensic case, particularly when there has been a fatal fire, as heat-related changes to bodies make identification by the forensic pathologist and other specialists difficult along with estimating the post-mortem interval and determining the precise cause and manner of death. We systematically reviewed all relevant articles dating from 2003 to 2022 in the PubMed database with a view to updating recommendations on how best to proceed. Our recommendations highlight the importance of a multidisciplinary team approach involving various forensic specialists.
Topics: Humans; Autopsy; Burns; Soft Tissue Injuries
PubMed: 37793642
DOI: 10.1177/00258172231191214 -
PeerJ. Computer Science 2024Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood...
BACKGROUND
Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, .
METHODOLOGY
This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs.
RESULTS
Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively.
CONCLUSION
The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.
PubMed: 38435563
DOI: 10.7717/peerj-cs.1813 -
BMC Medical Informatics and Decision... Jul 2023Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are...
INTRODUCTION
Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC.
METHODS
We conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL (", "Neural Networks (NN)", "Esophagus", "EC" and "Early Detection". After applying inclusion and exclusion criteria, 31 articles were retained for full review.
RESULTS
The results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods.
CONCLUSION
Our findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.
Topics: Humans; Deep Learning; Early Detection of Cancer; Machine Learning; Neural Networks, Computer; Esophageal Neoplasms
PubMed: 37460991
DOI: 10.1186/s12911-023-02235-y -
International Journal of Language &... 2023Individuals with affective-prosodic deficits have difficulty understanding or expressing emotions and attitudes through prosody. Affective prosody disorders can occur in... (Review)
Review
BACKGROUND
Individuals with affective-prosodic deficits have difficulty understanding or expressing emotions and attitudes through prosody. Affective prosody disorders can occur in multiple neurological conditions, but the limited knowledge about the clinical groups prone to deficits complicates their identification in clinical settings. Additionally, the nature of the disturbance underlying affective prosody disorder observed in different neurological conditions remains poorly understood.
AIMS
To bridge these knowledge gaps and provide relevant information to speech-language pathologists for the management of affective prosody disorders, this study provides an overview of research findings on affective-prosodic deficits in adults with neurological conditions by answering two questions: (1) Which clinical groups present with acquired affective prosodic impairments following brain damage? (2) Which aspects of affective prosody comprehension and production are negatively affected in these neurological conditions?
METHODS & PROCEDURES
We conducted a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. A literature search was undertaken in five electronic databases (MEDLINE, PsycINFO, EMBASE, CINAHL and Linguistics, and Language Behavior Abstracts) to identify primary studies reporting affective prosody disorders in adults with neurological impairments. We extracted data on clinical groups and characterised their deficits based on the assessment task used.
OUTCOMES & RESULTS
The review of 98 studies identified affective-prosodic deficits in 17 neurological conditions. The task paradigms typically used in affective prosody research (discrimination, recognition, cross-modal integration, production on request, imitation and spontaneous production) do not target the processes underlying affective prosody comprehension and production. Therefore, based on the current state of knowledge, it is not possible to establish the level of processing at which impairment occurs in clinical groups. Nevertheless, deficits in the comprehension of affective prosody are observed in 14 clinical groups (mainly recognition deficits) and deficits in the production of affective prosody (either on request or spontaneously) in 10 clinical groups. Neurological conditions and types of deficits that have not been investigated in many studies are highlighted.
CONCLUSIONS & IMPLICATIONS
The aim of this scoping review was to provide an overview on acquired affective prosody disorders and to identify gaps in knowledge that warrant further investigation. Deficits in the comprehension or production of affective prosody are common to numerous clinical groups with various neurological conditions. However, the underlying cause of affective prosody disorders across them is still unknown. Future studies should implement standardised assessment methods with specific tasks based on a cognitive model to identify the underlying deficits of affective prosody disorders.
WHAT THIS PAPER ADDS
What is already known on the subject What is already known on the subjectAffective prosody is used to share emotions and attitudes through speech and plays a fundamental role in communication and social interactions. Affective prosody disorders can occur in various neurological conditions, but the limited knowledge about the clinical groups prone to affective-prosodic deficits and about the characteristics of different phenotypes of affective prosody disorders complicates their identification in clinical settings. Distinct abilities underlying the comprehension and production of affective prosody can be selectively impaired by brain damage, but the nature of the disturbance underlying affective prosody disorders in different neurological conditions remains unclear. What this study adds Affective-prosodic deficits are reported in 17 neurological conditions, despite being recognised as a core feature of the clinical profile in only a few of them. The assessment tasks typically used in affective prosody research do not provide accurate information about the specific neurocognitive processes impaired in the comprehension or production of affective prosody. Future studies should implement assessment methods based on a cognitive approach to identify underlying deficits. The assessment of cognitive/executive dysfunctions, motor speech impairment and aphasia might be important for distinguishing primary affective prosodic dysfunctions from those secondarily impacting affective prosody. What are the potential clinical implications of this study? Raising awareness about the possible presence of affective-prosodic disorders in numerous clinical groups will facilitate their recognition by speech-language pathologists and, consequently, their management in clinical settings. A comprehensive assessment covering multiple affective-prosodic skills could highlight specific aspects of affective prosody that warrant clinical intervention.
Topics: Humans; Adult; Emotions; Speech Disorders; Aphasia; Linguistics; Language; Communication Disorders
PubMed: 37212522
DOI: 10.1111/1460-6984.12909 -
International Journal of Language &... 2023Contemporary clinical and empirical perspectives indicate that management of the psychosocial features of stuttering is fundamental for effective treatment.... (Review)
Review
BACKGROUND
Contemporary clinical and empirical perspectives indicate that management of the psychosocial features of stuttering is fundamental for effective treatment. Interventions that improve psychosocial outcomes for school-age children who stutter are, therefore, needed.
AIMS
This systematic review identifies what psychosocial outcomes have been explored in existing school-age clinical research, the measures used and the potential treatment effects. This will provide guidance for developing interventions that reflect contemporary perspectives of stuttering management.
METHODS & PROCEDURES
A total of 14 databases and three conference proceedings were searched for clinical reports of psychosocial outcomes of children aged 6-12 years. The review did not include pharmacological interventions. Psychosocial measures and outcomes were analysed in each study based on data recorded pre-treatment, immediately post-treatment and for any follow-up assessments.
MAIN CONTRIBUTIONS
Of the 4051 studies identified from the databases, a total of 22 studies met criteria for inclusion in the review. From these 22 studies, the review identified four prominent psychosocial domains that have been explored in school-age clinical research to date: Impact of stuttering, communication attitude, anxiety and speech satisfaction. These domains vary in measurement and effect sizes. Two behavioural treatments were associated with anxiety reduction, even though they did not contain anxiolytic procedures. No evidence of potential treatment effects emerged for communication attitudes. Quality of life-an important psychosocial domain pertinent to health economics-did not feature in school-age clinical reports.
CONCLUSIONS & IMPLICATIONS
The psychosocial features of stuttering need to be managed during the school years. Three psychosocial domains-impact of stuttering, anxiety and speech satisfaction-show evidence of potential treatment effects. This review provides direction for future clinical research so that speech-language pathologists can effectively and holistically manage school-age children who stutter.
WHAT THIS PAPER ADDS
What is already known on the subject Elevated levels of anxiety are apparent for children and adolescents who stutter. Therefore, the need to assess and manage psychosocial features of stuttering are expertly regarded as clinical priorities. Clinical trials of such psychosocial features of stuttering for children aged 6-12 years are not well advanced and, therefore, do not reflect current best practice management of this disorder. What this study adds to existing knowledge This systematic review identifies four different psychosocial domains measured and reported in the literature for school-age stuttering management. For three psychosocial domains, some evidence of potential treatment effects emerged with participant numbers greater than 10: Impact of stuttering, anxiety and speech satisfaction. Though treatment effect sizes varied, there is a suggestion that cognitive behaviour therapy can improve anxiety of school-age children who stutter. There is also suggestion that two other behavioural treatments can improve anxiety of school-age children who stutter. What are the potential or actual clinical implications of this work? Given the essential need for school-age children who stutter to receive management of any speech-related anxiety they may experience, it would be productive to discover in future clinical research what interventions could contribute to that goal-behavioural or psychosocial, or both. This review reveals that cognitive behaviour therapy, and other behavioural treatments, are associated with anxiety reductions. Such approaches should be considered for future clinical trial research to help advance the evidence base for managing school-age stuttering.
Topics: Adolescent; Humans; Child; Stuttering; Quality of Life; Speech; Anxiety; Communication
PubMed: 37132231
DOI: 10.1111/1460-6984.12887 -
International Journal of Molecular... Jan 2024Hypertrophic cardiomyopathy (HCM) is one of the most common genetic cardiovascular diseases, and it shows an autosomal dominant pattern of inheritance. HCM can be... (Review)
Review
Hypertrophic cardiomyopathy (HCM) is one of the most common genetic cardiovascular diseases, and it shows an autosomal dominant pattern of inheritance. HCM can be clinically silent, and sudden unexpected death due to malignant arrhythmias may be the first manifestation. Thus, the HCM diagnosis could be performed at a clinical and judicial autopsy and offer useful findings on morphological features; moreover, it could integrate the knowledge on the genetic aspect of the disease. This review aims to systematically analyze the literature on the main post-mortem investigations and the related findings of HCM to reach a well-characterized and stringent diagnosis; the review was performed using PubMed and Scopus databases. The articles on the post-mortem evaluation of HCM by gross and microscopic evaluation, imaging, and genetic test were selected; a total of 36 studies were included. HCM was described with a wide range of gross findings, and there were cases without morphological alterations. Myocyte hypertrophy, disarray, fibrosis, and small vessel disease were the main histological findings. The post-mortem genetic tests allowed the diagnosis to be reached in cases without morpho-structural abnormalities; clinical and forensic pathologists have a pivotal role in HCM diagnosis; they contribute to a better definition of the disease and also provide data on the genotype-phenotype correlation, which is useful for clinical research.
Topics: Humans; Cardiomyopathy, Hypertrophic; Genetic Testing; Arrhythmias, Cardiac; Autopsy; Fibrosis; Phenotype; Death, Sudden, Cardiac
PubMed: 38279275
DOI: 10.3390/ijms25021275 -
Journal of Pathology Informatics Dec 2024Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important... (Review)
Review
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review.
Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of their generalizability. The aim of this systematic review was to assess the performance of externally validated ML models based on histopathology images for diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer. A systematic search of MEDLINE, EMBASE, CINAHL, IEEE, MICCAI, and SPIE conferences was performed for studies published between January 2010 and February 2022. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed, and the results were narratively described. Of the 2011 non-duplicated citations, 8 journal articles and 2 conference proceedings met inclusion criteria. Three studies externally validated ML models for diagnosis, 4 for classification, 2 for prognosis, and 1 for both classification and prognosis. Most studies used Convolutional Neural Networks and one used logistic regression algorithms. For diagnostic/classification models, the most common performance metrics reported in the EV were accuracy and area under the curve, which were greater than 87% and 90%, respectively, using pathologists' annotations/diagnoses as ground truth. The hazard ratios in the EV of prognostic ML models were between 1.7 (95% CI, 1.2-2.6) and 1.8 (95% CI, 1.3-2.7) to predict distant disease-free survival; 1.91 (95% CI, 1.11-3.29) for recurrence, and between 0.09 (95% CI, 0.01-0.70) and 0.65 (95% CI, 0.43-0.98) for overall survival, using clinical data as ground truth. Despite EV being an important step before the clinical application of a ML model, it hasn't been performed routinely. The large variability in the training/validation datasets, methods, performance metrics, and reported information limited the comparison of the models and the analysis of their results. Increasing the availability of validation datasets and implementing standardized methods and reporting protocols may facilitate future analyses.
PubMed: 38089005
DOI: 10.1016/j.jpi.2023.100348 -
International Journal of Molecular... May 2024Eosinophilic solid and cystic renal cell carcinoma (ESC-RCC) is a novel and uncommon type of renal cell carcinoma, which has been recently recognized and introduced as a... (Meta-Analysis)
Meta-Analysis
Eosinophilic solid and cystic renal cell carcinoma (ESC-RCC) is a novel and uncommon type of renal cell carcinoma, which has been recently recognized and introduced as a distinct entity in the WHO 2022 kidney tumor classification. Previously known as "unclassified RCC", followed by "tuberous sclerosis complex (TSC)-associated RCC", ESC-RCC is now a distinct category of kidney tumor, with its own name, with specific clinical manifestations, and a unique morphological, immunohistochemical and molecular profile. Due to its recent introduction and the limited available data, the diagnosis of ESC-RCC is still a complex challenge, and it is probably frequently misdiagnosed. The secret of diagnosing this tumor lies in the pathologists' knowledge, and keeping it up to date through research, thereby limiting the use of outdated nomenclature. The aim of our case-based review is to provide a better understanding of this pathology and to enrich the literature with a new case report, which has some particularities compared to the existing cases.
Topics: Humans; Carcinoma, Renal Cell; Kidney Neoplasms; Eosinophilia; Male
PubMed: 38892169
DOI: 10.3390/ijms25115982