Model Dermatol – Skin Disease

Iderma

Mayelana nalolu hlelo lokusebenza

I-Artificial intelligence isikenazithombe ezinikeziwe futhi isize ngokushesha ukusiza inkinga yesikhumba sakho. I-AI inikeza ulwazi olufanelekwezokwelapha ngezifo zesikhumba (isib., ukubonakala kwesikhumba, insumpa, isidleke) kanye nomdlavuza wesikhumba (isib., i-melanoma).

◉ Thatha izithombe zesikhumba bese uzihambisa. Izithombe ezisikiwe ziyadluliswa, kodwa asiyigcini idatha yakho.
◉ I-AI inikeza izixhumanisi kumawebhusayithi achaza izimpawu nezimpawu ezifanelekwezifo sesikhumba nomdlavuza wesikhumba (isib., i-melanoma).
◉ I-algorithm ingahlukanisa izithombe zezifo zesikhumba eziyi-186, okuhlanganisa izinhlobo ezivamile zokuphazamiseka kwesikhumba (isib., i-atopic dermatitis, i-hive, i-eczema, i-psoriasis, i-acne, i-rosacea, i-onychomycosis, i-melanoma, i-nevus).
◉ Ukusetshenziswa kwe-algorithm kumahhala kanye nezilimi eziyi-104 zisekelwe.

🞹 Ukushicilelwa
Sisebenzisa i-algorithm ethi "Model Dermatology". Ukusebenza komhleli kushicilelwe kumajenali wezokwelapha ahlonishwayo.
- Assessment of Deep Neural Networks for the Diagnosis of Benign and Malignant Skin Neoplasms in Comparison with Dermatologists: A Retrospective Validation Study. PLOS Medicine, 2020
- Performance of a deep neural network in teledermatology: a single center prospective diagnostic study. J Eur Acad Dermatol Venereol. 2020
- Keratinocytic Skin Cancer Detection on the Face using Region-based Convolutional Neural Network. JAMA Dermatol. 2019
- Seems to be low, but is it really poor? : Need for Cohort and Comparative studies to Clarify Performance of Deep Neural Networks. J Invest Dermatol. 2020
- Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement. J Invest Dermatol. 2020
- Augment Intelligence Dermatology : Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders. J Invest Dermatol. 2020
- Interpretation of the Outputs of Deep Learning Model trained with Skin Cancer Dataset. J Invest Dermatol. 2018
- Automated Dermatological Diagnosis: Hype or Reality? J Invest Dermatol. 2018
- Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J Invest Dermatol. 2018
- Augmenting the Accuracy of Trainee Doctors in Diagnosing Skin Lesions Suspected of Skin Neoplasms in a Real-World Setting: A Prospective Controlled Before and After Study. PLOS One, 2022
- Evaluation of Artificial Intelligence-assisted Diagnosis of Skin Neoplasms – a single-center, paralleled, unmasked, randomized controlled trial. J Invest Dermatol. 2022

🞹 Umshwana wokuzihlangula
- Sicela ufune iseluleko sikadokotela ngaphezu kokusebenzisa lolu hlelo lokusebenza nangaphambi kokwenza noma yiziphi izinqumo zezokwelapha.
- Ukuxilongwa komdlavuza wesikhumba noma ukuphazamiseka kwesikhumba okusekelwe kuphela ezithombeni zomtholampilo kungase kugeje kufikela ku-10% wamacala. Ngakho-ke, lolu hlelo lokusebenza alukwazi ukumiselela ukunakekelwa okujwayelekile (ukuhlolwa komuntu siqu).
- Ukubikezela kwe-algorithm akukona ukuxilongwa kokugcina komdlavuza wesikhumba noma ukuphazamiseka kwesikhumba. Isebenza kuphela ukuhlinzeka ngolwazi lwezokwelapha lomuntu siqu ukuze lusetshenziswe