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The trichogram is a practical, non-invasive, and cost-effective tool for diagnosing female androgenetic alopecia.

Glycated hemoglobin is not as effective as the oral glucose tolerance test for diagnosing blood sugar problems in women with PCOS in Southern Iraq.

Low SHBG and high FAI, FT, and DHEAS levels are effective in identifying PCOS.

5ARIs lower PSA levels, but other tests are needed to avoid missing aggressive prostate cancer.

The AI showed high accuracy in diagnosing skin conditions but needs improvement for immunological and infectious disorders.

HPLC may detect prediabetes and diabetes earlier than Immunoturbidimetry because it shows higher A1c levels.

The IGRA test effectively detects past SARS-CoV-2 exposure, especially using nucleocapsid peptides.

A new method accurately measures nine neuroactive steroids in small blood samples, helping to study brain diseases.

The model accurately identifies hair diseases using deep learning.

A web platform was created to help diagnose scalp conditions accurately and easily.

A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.

The AI model DIET-AI effectively diagnoses skin diseases as well as doctors.

Machine learning can accurately diagnose PCOS non-invasively using clinical and ultrasound features.

CD27 and IL-35 can help diagnose alopecia areata linked to bacterial infections.

The Random Forest algorithm was the most accurate at diagnosing Polycystic Ovarian Syndrome.

The random forest model effectively helps diagnose COVID-19 using key factors like age and symptoms.

The expert system effectively helps diagnose and treat hair loss conditions.

Machine learning can improve early and accurate detection of PCOS.

Microextraction techniques improve hormone testing while being environmentally friendly.

Higher cortisol levels and stress are linked to worse cognition and more fatigue in MS patients.

The algorithm effectively removes hair from skin images, improving melanoma diagnosis accuracy.

The new AI system improves remote skin condition diagnosis and access to care.

Polarized light microscopy is better at distinguishing scarring from non-scarring alopecia than diffractive microscopy.

HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.

Female athletes using steroids risk hair loss and need better education and treatment.

The new model improves Alopecia Areata classification accuracy to 93.1%.

AI models are effective for detecting alopecia areata but face challenges like explaining results and data bias.

Athletes need effective management of skin disorders for their performance and well-being.

The method accurately detects and removes hair from skin images to improve melanoma diagnosis.

An AI device for skin cancer was successfully integrated into the NHS, improving diagnosis accuracy and service capacity.