April 2023 in “Journal of Investigative Dermatology” The MDhair app accurately assesses hair loss severity with 94% accuracy.
September 2022 in “Research Square (Research Square)” The AI model DIET-AI effectively diagnoses skin diseases as well as doctors.
April 2023 in “Journal of Investigative Dermatology” The improved EczemaNet more reliably and clearly identifies and assesses the severity of atopic dermatitis from photos.
5 citations,
February 2015 in “Dermatologica Sinica” Computer-aided imaging system helps measure balding area in female pattern hair loss.
20 citations,
December 2017 in “Journal of Investigative Dermatology Symposium Proceedings” Researchers created a fast, accurate computer program to measure hair loss in alopecia areata patients.
1 citations,
January 2023 in “IEEE access” Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.
The new algorithm removes hair from skin images better than previous methods, helping diagnose melanoma.
1 citations,
January 2024 in “IEEE access” The new method improves facial image restoration quality and face recognition accuracy.
1 citations,
October 2023 Syntax-based neural networks can match Transformers in handling unseen sentences.
June 2023 in “International journal on recent and innovation trends in computing and communication” Combining multiple algorithms predicts hair fall more accurately than using single algorithms.
Pre-trained Transformers need extreme retraining to perform well on DarkNet data.
6 citations,
January 2018 in “Multimedia Tools and Applications” The new method removes hair from skin images quickly and accurately to help identify skin lesions better.
January 2024 in “Wiadomości Lekarskie” Robotic hair transplantation with AI offers more reliable, precise, and efficient hair restoration.
42 citations,
November 2019 in “Frontiers in Endocrinology” The document suggests creating a validated score to diagnose Cushing's Syndrome and considers plasma steroid profiling as a simpler diagnostic method.
30 citations,
October 2020 in “Nature Communications” Finasteride irreversibly affects human steroid 5α-reductase 2, providing insight into its catalytic mechanism and disease-related mutations.
14 citations,
March 2019 in “Plant methods” The new microrhizotron tool effectively observes and measures pepper plant roots non-destructively.
10 citations,
September 2020 in “Computational and Mathematical Methods in Medicine” Researchers developed an algorithm for self-diagnosing scalp conditions with high accuracy using smart device-attached microscopes.
2 citations,
January 2019 in “Medizinische Genetik” The document reports findings on genetic research, including ethical concerns about genome editing, improved diagnosis of mitochondrial mutations, solving inherited eye diseases, confirming gene roles in epilepsy, linking a gene to aneurysms, and identifying genes associated with age-related macular degeneration.
2 citations,
February 2009 in “Journal of the American Academy of Dermatology” Many research paper titles in dermatology journals lack scientific precision.
1 citations,
February 2024 in “npj digital medicine” Researchers improved a skin disease diagnosis model using online images, achieving up to 49.64% accuracy.
The method creates realistic, anonymous acne face images for research, achieving 97.6% accuracy in classification.
February 2024 in “Frontiers in physics” The new model detects hair clusters more accurately and efficiently, helping with early hair loss treatment and diagnosis.
OCT can effectively screen and diagnose various medical conditions non-invasively.
December 2017 in “The journal of investigative dermatology. Symposium proceedings/The Journal of investigative dermatology symposium proceedings” The summit aimed to speed up finding treatments for alopecia areata.
September 2024 in “Journal of Investigative Dermatology” A new tool can analyze hair to detect changes due to hormones, genetics, and aging.
8 citations,
January 2022 in “Sensors” Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.
Transfer learning with three neural network architectures accurately classifies hair diseases.
April 2019 in “The journal of investigative dermatology/Journal of investigative dermatology” Higher resolution images are needed to identify scarring and fine hair in alopecia.
January 2022 in “Sustainable development goals series” The document concludes that significant investment in agricultural innovation is necessary to achieve global food security and nutrition.