There are many sources online that prove the increasing use of technology in healthcare and a shift in patient culture reflecting patients' desire to have more control of their health/healthcare. Virtual medicine is increasing accessibility of high-quality healthcare to rural regions and remote patients.
Check out these articles about telehealth for starters: https://www.healthcarefinancenews.com/news/hospitals-are-turning-telehealth-manage-scarce-physician-resources https://www.theguardian.com/healthcare-network/2013/nov/12/telehealth-important-role-future-healthcare https://www.mayoclinic.org/healthy-lifestyle/consumer-health/in-depth/telehealth/art-20044878
I wanted to go one step further and imagine a future where patients can take health into their own hands. With this project, I intended to create a prototype of one such example. A convolutional neural network was trained on a subset of skin cancer images (dataset used is: https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000). The dataset includes a total of 10,015 dermatoscopic images from different populations, acquired and stored by different modalities. The images are representative of 7 diagnoses - Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc) - along with corresponding parameters (age, gender, localization of lesion). Users can then upload an image of a lesion they are concerned about and the trained model will output a diagnosis with a confidence interval. There were some challenges with training the model - images of skin are pretty difficult to differentiate and classify, plus overfitting was a constant battle (out of 10k images, 7k were 'nv' so the data was biased towards 'nv'). To account for overfitting, 6,000 images of 'nv' were removed, the training and validation sample set was reduced to 4,000, and the model was trained on 80% of the 4,000 sample set. This resulted in about 66% accuracy for predictions. Amazon AWS RDS MySQL used to host the database and AWS EC2 used to create a server for the web application.
In addition to the model, I wanted to create a clean dashboard that is clinical but inviting. I imagine that a user interface that is easy to navigate and open will invite more users to take initiative with their health.
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T • Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesion. Sci. Data 5, 180161 (2018). doi: 10.1038/sdata.2018.161
Released Under CC BY-NC-SA 4.0 . Please refer to Creative Commons Attribution - NonCommercial 4.0 International Public License located in Terms tab of the original data source link for information on the Public License and disclaimer of warranties.