Assessing kidney stone composition using smartphone microscopy and deep neural networks

Abstract

Objectives: To propose a point-of-care image recognition system for kidney stone composition classification using smartphone microscopy and deep convolutional neural networks. Materials and methods:A total of 37 surgically extracted human kidney stones consisting of calcium oxalate (CaOx), cystine, uric acid (UA) and struvite stones were included in the study. All of the stones were fragmented from percutaneous nephrolithotomy (PCNL). The stones were classified using Fourier transform infrared spectroscopy (FTIR) analysis before obtaining smartphone microscope images. The size of the stones ranged from 5 to 10 mm in diameter. Nurugo 400Â smartphone microscope (Nurugo, Seoul, Republic of Korea) was functionalized to acquire microscopic images (magnification = 25Â) of dry kidney stones using iPhone 6s+ (Apple, Cupertino, CA, USA). Each kidney stone was imaged in six different locations. In total, 222 images were captured from 37 stones. A novel convolutional neural network architecture was built for classification, and the model was assessed using accuracy, positive predictive value, sensitivity and F1 scores. Results:We achieved an overall and weighted accuracy of 88% and 87%, respectively, with an average F1 score of 0.84. The positive predictive value, sensitivity and F1 score for each stone type were respectively reported as follows: CaOx (0.82, 0.83, 0.82), cystine (0.80, 0.88, 0.84), UA (0.92, 0.77, 0.85) and struvite (0.86, 0.84, 0.85). Conclusion:We demonstrate a rapid and accurate point of care diagnostics method for classifying the four types of kidney stones. In the future, diagnostic tools that combine smartphone microscopy with artificial intelligence (AI) can provide accessible health care that can support physicians in their decision-making process.

Theranostic roles of machine learning in clinical management of kidney stone disease

“…El Beze et al [67] have developed an automated stone detection technique to discriminate six types of stones from endoscopy by using surface and section of urinary calculi. Using a dataset of smartphone-based microscopic images, Onal et al [68] have evaluated an image recognition system for categorizing four types of kidney stones in the rapid and precise manner. Likewise, Estrade et al [69] have applied deep learning method on digital endoscopic video sequences to automatically detect stone morphology during the stone fragmentation process.…”

Seasonal events and journal milestones

“…For this section, we feature a different form of innovation than our typical surgical techniques. Onal et al [8] present a novel method of…”

“…For this section, we feature a different form of innovation than our typical surgical techniques. Onal et al [8] present a novel method of determining kidney stone composition using smartphone microscopy and neural networks with accuracies >85%. The figures are unique—from a review of stone compositions with microscopy, to the machine learning methods.…”

Artificial intelligence in the diagnosis and treatment of kidney stone disease.

“…Интересным также представляется исследование Onal и соавт., которые не только разработали алгоритм, позволяющий определить химический состав конкрементов, но и интегрировали его в работу смартфона [25]. В исследование было включено 37 удаленных хирургическим путем камней, состоящих из оксалата кальция, цистина, мочевой кислоты и струвита.…”