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Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions. A deep learning system able to identify the most common skin conditions may help clinicians in making more accurate diagnoses in routine clinical practice

\"Over 900 tests help you reach a diagnosis! The new fourth edition of Laboratory Tests and Diagnostic Procedures is the most complete reference of its kind. Part One consists of a unique alphabetical list of more than 600 diseases, conditions, and symptoms, paired with the tests and procedures commonly used to rule out or confirm each condition. Part Two represents an up-to-date, concise, complete compilation of virtually every laboratory and diagnostic test available to clinicians today.\"--Jacket.

Plasma tau phosphorylated at threonine 217 (p-tau217) and plasma tau phosphorylated at threonine 181 (p-tau181) are associated with Alzheimer's disease tau pathology. We compared the diagnostic value of both biomarkers in cognitively unimpaired participants and patients with a clinical diagnosis of mild cognitive impairment, Alzheimer's disease syndromes, or frontotemporal lobar degeneration (FTLD) syndromes. In this retrospective multicohort diagnostic performance study, we analysed plasma samples, obtained from patients aged 18–99 years old who had been diagnosed with Alzheimer's disease syndromes (Alzheimer's disease dementia, logopenic variant primary progressive aphasia, or posterior cortical atrophy), FTLD syndromes (corticobasal syndrome, progressive supranuclear palsy, behavioural variant frontotemporal dementia, non-fluent variant primary progressive aphasia, or semantic variant primary progressive aphasia), or mild cognitive impairment; the participants were from the University of California San Francisco (UCSF) Memory and Aging Center, San Francisco, CA, USA, and the Advancing Research and Treatment for Frontotemporal Lobar Degeneration Consortium (ARTFL; 17 sites in the USA and two in Canada). Participants from both cohorts were carefully characterised, including assessments of CSF p-tau181, amyloid-PET or tau-PET (or both), and clinical and cognitive evaluations. Plasma p-tau181 and p-tau217 were measured using electrochemiluminescence-based assays, which differed only in the biotinylated antibody epitope specificity. Receiver operating characteristic analyses were used to determine diagnostic accuracy of both plasma markers using clinical diagnosis, neuropathological findings, and amyloid-PET and tau-PET measures as gold standards. Difference between two area under the curve (AUC) analyses were tested with the Delong test. Data were collected from 593 participants (443 from UCSF and 150 from ARTFL, mean age 64 years [SD 13], 294 [50%] women) between July 1 and Nov 30, 2020. Plasma p-tau217 and p-tau181 were correlated (r=0·90, p<0·0001). Both p-tau217 and p-tau181 concentrations were increased in people with Alzheimer's disease syndromes (n=75, mean age 65 years [SD 10]) relative to cognitively unimpaired controls (n=118, mean age 61 years [SD 18]; AUC=0·98 [95% CI 0·95–1·00] for p-tau217, AUC=0·97 [0·94–0·99] for p-tau181; pdiff=0·31) and in pathology-confirmed Alzheimer's disease (n=15, mean age 73 years [SD 12]) versus pathologically confirmed FTLD (n=68, mean age 67 years [SD 8]; AUC=0·96 [0·92–1·00] for p-tau217, AUC=0·91 [0·82–1·00] for p-tau181; pdiff=0·22). P-tau217 outperformed p-tau181 in differentiating patients with Alzheimer's disease syndromes (n=75) from those with FTLD syndromes (n=274, mean age 67 years [SD 9]; AUC=0·93 [0·91–0·96] for p-tau217, AUC=0·91 [0·88–0·94] for p-tau181; pdiff=0·01). P-tau217 was a stronger indicator of amyloid-PET positivity (n=146, AUC=0·91 [0·88–0·94]) than was p-tau181 (n=214, AUC=0·89 [0·86–0·93]; pdiff=0·049). Tau-PET binding in the temporal cortex was more strongly associated with p-tau217 than p-tau181 (r=0·80 vs r=0·72; pdiff<0·0001, n=230). Both p-tau217 and p-tau181 had excellent diagnostic performance for differentiating patients with Alzheimer's disease syndromes from other neurodegenerative disorders. There was some evidence in favour of p-tau217 compared with p-tau181 for differential diagnosis of Alzheimer's disease syndromes versus FTLD syndromes, as an indication of amyloid-PET-positivity, and for stronger correlations with tau-PET signal. Pending replication in independent, diverse, and older cohorts, plasma p-tau217 and p-tau181 could be useful screening tools to identify individuals with underlying amyloid and Alzheimer's disease tau pathology. US National Institutes of Health, State of California Department of Health Services, Rainwater Charitable Foundation, Michael J Fox foundation, Association for Frontotemporal Degeneration, Alzheimer's Association.

Accurate diagnosis of multiple sclerosis requires careful attention to its differential diagnosis—many disorders can mimic the clinical manifestations and paraclinical findings of this disease. A collaborative effort, organised by The International Advisory Committee on Clinical Trials in Multiple Sclerosis in 2008, provided diagnostic approaches to multiple sclerosis and identified clinical and paraclinical findings (so-called red flags) suggestive of alternative diagnoses. Since then, knowledge of disorders in the differential diagnosis of multiple sclerosis has expanded substantially. For example, CNS inflammatory disorders that present with syndromes overlapping with multiple sclerosis can increasingly be distinguished from multiple sclerosis with the aid of specific clinical, MRI, and laboratory findings; studies of people misdiagnosed with multiple sclerosis have also provided insights into clinical presentations for which extra caution is warranted. Considering these data, an update to the recommended diagnostic approaches to common clinical presentations and key clinical and paraclinical red flags is warranted to inform the contemporary clinical evaluation of patients with suspected multiple sclerosis.

\"Improved diagnostic sophistication is increasingly enabling neurologists to differentiate between Parkinson's disease and other atypical parkinsonism (AP), such as multiple system atrophy, progressive supranuclear palsy, corticobasal degeneration, and dementia with Lewy bodies. The Handbook of Atypical Parkinsonism is a comprehensive survey of all diseases of this category, providing an authoritative guide to the recognition, diagnosis and management of these disorders. Each chapter follows a common structure, commencing with the full basic science of the disorder under consideration, followed by descriptions of the clinical picture and differential diagnosis. Subsequent chapters discuss current and future therapeutic approaches to these difficult conditions. Written and edited by leading practitioners in the field, clinicians in neurology and other specialties will find this book essential to the understanding and diagnosis of this complex group of disorders\"--Provided by publisher.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care. Drawing on 51,269 participants across 9 independent, geographically diverse datasets, an AI model identifies the etiologies contributing to dementia in individuals, harnessing a broad array of data, including demographics, medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging.

Myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS) are serious, debilitating conditions that affect millions of people in the United States and around the world. ME/CFS can cause significant impairment and disability. Despite substantial efforts by researchers to better understand ME/CFS, there is no known cause or effective treatment. Diagnosing the disease remains a challenge, and patients often struggle with their illness for years before an identification is made. Some health care providers have been skeptical about the serious physiological - rather than psychological - nature of the illness. Once diagnosed, patients often complain of receiving hostility from their health care provider as well as being subjected to treatment strategies that exacerbate their symptoms. Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome proposes new diagnostic clinical criteria for ME/CFS and a new term for the illness - systemic exertion intolerance disease(SEID). According to this report, the term myalgic encephalomyelitis does not accurately describe this illness, and the term chronic fatigue syndrome can result in trivialization and stigmatization for patients afflicted with this illness. Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome stresses that SEID is a medical - not a psychiatric or psychological - illness. This report lists the major symptoms of SEID and recommends a diagnostic process.One of the report's most important conclusions is that a thorough history, physical examination, and targeted work-up are necessary and often sufficient for diagnosis. The new criteria will allow a large percentage of undiagnosed patients to receive an accurate diagnosis and appropriate care. Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome will be a valuable resource to promote the prompt diagnosis of patients with this complex, multisystem, and often devastating disorder; enhance public understanding; and provide a firm foundation for future improvements in diagnosis and treatment.