AIMC Topic: Diagnosis, Differential

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Cracking the code: a head-to-head comparison of expert clinicians and artificial intelligence in diagnosing rare diseases.

Orphanet journal of rare diseases
BACKGROUND: Patients with rare diseases often face prolonged diagnostic journeys due to the low prevalence and diverse clinical presentations of these conditions. In Germany, specialized centers for rare diseases, established at university hospitals,...

CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study.

Biomedical engineering online
OBJECTIVES: This study aimed to develop and validate a CT radiomics-based explainable machine learning model for precise diagnosing of malignancy and benignity specifically in endometrial cancer (EC) patients.

Interpretable radiomics-based machine learning model for differentiating glioblastoma from primary central nervous system lymphoma using contrast-enhanced T1-weighted imaging.

Scientific reports
This study aimed to develop and validate an interpretable radiomics-based machine learning model using contrast-enhanced T1-weighted imaging (CE-T1WI) to differentiate glioblastoma (GB) from primary central nervous system lymphoma (PCNSL), while comp...

Deep learning-based non-invasive differential diagnosis of eyelid basal cell and sebaceous gland carcinomas using photographic images.

International ophthalmology
PURPOSE: Pathological examination, the current gold standard for differentiating eyelid basal cell carcinoma (BCC) and sebaceous gland carcinoma (SGC), is invasive, time-consuming, and often inaccessible in primary care hospitals. Therefore, a non-in...

Artificial intelligence based sonographic differentiation between skull fractures and normal sutures in young children.

Scientific reports
Accurate differentiation between skull fractures and sutures is challenging in young children. Traditional diagnostic modalities like computed tomography involve ionizing radiation, while sonography is safer but demands expertise. This study explores...

Differentiation of optic disc edema and pseudopapilledema with deep learning on near-infrared reflectance images.

BMC ophthalmology
BACKGROUND: This study aimed to develop an artificial intelligence-based deep learning (DL) algorithm using near-infrared reflectance (NIR) images to differentiate between optic disc edema and pseudopapilledema, and to evaluate the diagnostic perform...

A comparison of the performance of Chinese large language models and ChatGPT throughout the entire clinical workflow.

Scientific reports
BACKGROUND: ChatGPT has demonstrated strong performance in the complex, full clinical workflow. In recent years, several large language models (LLMs) from China have been introduced; however, their performance in such intricate tasks has yet to be th...

Rapid discrimination of and non-tuberculous mycobacteria disease via interpretive machine learning analysis of routine laboratory tests.

BMJ health & care informatics
OBJECTIVES: Rapid discrimination of infections caused by (MTB) and non-tuberculous mycobacteria (NTM) is crucial in clinical settings. Despite overlapping clinical and radiological features, the two require markedly different therapeutic approaches ...

Machine learning model to classify chronic leg wounds and identify pyoderma gangrenosum.

BMJ health & care informatics
STUDY OBJECTIVES: Chronic wounds represent a significant economic and personal burden. For their successful treatment, the causes must be known and treated. Wounds caused by pyoderma gangrenosum (PG), a rare inflammatory skin disease, are often misdi...

Natural lithium isotope variations in serum after lithium administration as a novel biomarker for differentiating schizophrenia and bipolar disorder.

Translational psychiatry
Accurate differentiation of schizophrenia (SZ) and bipolar disorder (BD) is crucial for effective clinical management. However, current diagnostic methods, which rely heavily on subjective assessments, are prone to high rates of misdiagnosis. This st...