Type | Variant | ||||
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12.1.59 12159 APKs | |||||
Size: 28.72 MB Certificate: 08690132c3e4ac4f6dedffc96ee9a1bbfc69751a SHA1 signature: 0d871f8df9d4b9d689d93aa5e0901177447e6359 Architecture: universal Screen DPI: ldpi (120dpi), mdpi (160dpi), tvdpi (213dpi), hdpi (240dpi), xhdpi (320dpi), xxhdpi (480dpi), xxxhdpi (640dpi) Device: phone | |||||
12.1.59 12159 APK | |||||
Size: 28.25 MB Certificate: 08690132c3e4ac4f6dedffc96ee9a1bbfc69751a SHA1 signature: d25ef4a336783891d0af84d36cbe1326f1a27d2c Architecture: universal Screen DPI: ldpi (120dpi), mdpi (160dpi), hdpi (240dpi), xhdpi (320dpi), xxhdpi (480dpi), xxxhdpi (640dpi) Device: phone |
Download Model Dermatology APK free
Artificial intelligence for screening high-risk patients in skin cancer
Artificial intelligence scans the given photos and instantly advises on your skin problems. AI gives risk information of skin cancer associated with the specific lesion and determines the appropriate type or level of medical care. * Model Dermatology is approved as a medical device (CE MDR Class I) on 22 June 2021
♦ Capture skin photographs and submit.
♦ Model Dermatology can classify 184 skin diseases.
♦ Model Dermatology will provide information on skin disease and dermatology clinics. AI provides links to websites that describe the signs and symptoms of skin diseases.
♦ Model Dermatology will perform the visual assessment of the lesion and give risk information of skin cancer (e.g. melanoma).
♦ High Speed Oversea Internet Connection is required to run the online algorithm. Please check the internet connection if it does not work.
The algorithm can classify 184 skin diseases which include most kinds of skin cancers and inflammatory disorders (e.g. skin rash). The performance of the skin disease classifier was published in several medical journals.
** Papers for the Model Dermatology **
- 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
** Disclaimer **
- Please consult with your doctor for an accurate diagnosis.
- A total of 10% cases of skin cancer can be missed if the diagnosis was made using images alone, therefore, this app can not substitute the role of standard care.
- Please seek a doctor’s advice in addition to using this app and before making any medical decisions.
♦ Capture skin photographs and submit.
♦ Model Dermatology can classify 184 skin diseases.
♦ Model Dermatology will provide information on skin disease and dermatology clinics. AI provides links to websites that describe the signs and symptoms of skin diseases.
♦ Model Dermatology will perform the visual assessment of the lesion and give risk information of skin cancer (e.g. melanoma).
♦ High Speed Oversea Internet Connection is required to run the online algorithm. Please check the internet connection if it does not work.
The algorithm can classify 184 skin diseases which include most kinds of skin cancers and inflammatory disorders (e.g. skin rash). The performance of the skin disease classifier was published in several medical journals.
** Papers for the Model Dermatology **
- 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
** Disclaimer **
- Please consult with your doctor for an accurate diagnosis.
- A total of 10% cases of skin cancer can be missed if the diagnosis was made using images alone, therefore, this app can not substitute the role of standard care.
- Please seek a doctor’s advice in addition to using this app and before making any medical decisions.
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More info
Updated in
2021-08-05
Size
28.2 MB
Current version
12.1.59
Requires Android
5.1 and up
Content Rating
Everyone
Offered By
IDerma
Developer [email protected]