Background: Intraoperative pathology consultation plays a crucial role in tumor surgery. The ability to accurately and rapidly distinguish tumor from normal tissue can greatly impact intraoperative surgical oncology management. However, this is dependent on the availability of a specialized pathologist for a reliable diagnosis. We developed and prospectively validated an artificial intelligence-based smartphone app capable of differentiating between pituitary adenoma and normal pituitary gland using stimulated Raman histology, almost instantly.
Methods: The study consisted of three parts. After data collection (part 1) and development of a deep learning-based smartphone app (part 2), we conducted a prospective study that included 40 consecutive patients with 194 samples to evaluate the app in real-time in a surgical setting (part 3). The smartphone app's sensitivity, specificity, positive predictive value, and negative predictive value were evaluated by comparing the diagnosis rendered by the app to the ground-truth diagnosis set by a neuropathologist.
Results: The app exhibits a sensitivity of 96.1% (95% CI: 89.9-99.0%), specificity of 92.7% (95% CI: 74-99.3%), positive predictive value of 98% (95% CI: 92.2-99.8%), and negative predictive value of 86.4% (95% CI: 66.2-96.8%). An external validation of the smartphone app on 40 different adenoma tumors and a total of 191 scanned SRH specimens from a public database shows a sensitivity of 93.7% (95% CI: 89.3-96.7%).
Conclusions: The app can be readily expanded and repurposed to work on different types of tumors and optical images. Rapid recognition of normal versus tumor tissue during surgery may contribute to improved intraoperative surgical management and oncologic outcomes. In addition to the accelerated pathological assessments during surgery, this platform can be of great benefit in community hospitals and developing countries, where immediate access to a specialized pathologist during surgery is limited.
In tumor surgery, precise identification of abnormal tissue during surgical removal of the tumor is paramount. Traditional methods rely on the availability of specialized pathologists for a reliable diagnosis, which could be a limitation in many hospitals. Our study introduces a user-friendly smartphone app that quickly and precisely diagnoses pituitary tumors, powered by artificial intelligence (AI), which is the simulation of human intelligence in machines for tasks like learning, reasoning, problem-solving, and decision-making. Through data collection, app development, and validation, our findings demonstrate that the app can rapidly and accurately identify tumors in real-time. External validation further confirmed its effectiveness in detecting tumor tissue collected from a different source. This AI-driven app could contribute to elevating surgical precision, particularly in settings lacking immediate access to specialized pathologists.
© 2024. The Author(s).