Background
The integration of machine learning algorithms in decision support tools for physicians is gaining popularity. These tools can tackle the disparities in healthcare access as the technology can be implemented on smartphones. We present the first, large‐scale study on patients with skin of colour, in which the feasibility of a novel mobile health application (mHealth app) was investigated in actual clinical workflows.
Objective
To develop a mHealth app to diagnose 40 common skin diseases and test it in clinical settings.
Methods
A convolutional neural network‐based algorithm was trained with clinical images of 40 skin diseases. A smartphone app was generated and validated on 5014 patients, attending rural and urban outpatient dermatology departments in India. The results of this mHealth app were compared against the dermatologists’ diagnoses.
Results
The machine–learning model, in an in silico validation study, demonstrated an overall top‐1 accuracy of 76.93 ± 0.88% and mean area‐under‐curve of 0.95 ± 0.02 on a set of clinical images. In the clinical study, on patients with skin of colour, the app achieved an overall top‐1 accuracy of 75.07% (95% CI = 73.75–76.36), top‐3 accuracy of 89.62% (95% CI = 88.67–90.52) and mean area‐under‐curve of 0.90 ± 0.07.
Conclusion
This study underscores the utility of artificial intelligence‐driven smartphone applications as a point‐of‐care, clinical decision support tool for dermatological diagnosis for a wide spectrum of skin diseases in patients of the skin of colour.
Segmentation of abnormal masses in kidney images is a tough task. One of the main challenges is the presence of speckle noise, which will restrain the valuable information for the medical practitioners. Hence, the detection and segmentation of the affected regions vary in accuracies. The proposed model includes pre-processing and segmentation of the diseased region. The pre-processing consists of Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLHE) to improve the clarity of the images. Further, segmentation has been done based on the entropy of the image and gamma correction has been done to improve the overall brightness of the images. An optimal global threshold value is selected to extract the region of interest and measures the area. The model is analyzed with statistical parameters like Jaccard index and Dice coefficient and compared with the ground truth images. To check the accuracy of the segmentation, relative error is calculated. This framework can be used by radiologists in diagnosing kidney patients
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