Computer vision and AI driven data processing have triggered unprecedented experiences in the medical field and particularly in medical imaging. The synergy of computer vision and artificial intelligence in the various aspects of medical image enhancement enables reliable and efficient result analysis, enhanced surgical robot manipulation and many other advancements.

The combination of deep learning and medical imaging is driving countless innovations in radiography, histopathology and numerous other fields. Advancing and automating the in-depth analysis of digital imaging greatly benefit medical diagnostics, including the detection of cancer cells, blood disorders, ocular diseases, etc. Patients also benefit from revolutionary advancements in surgical robots. Apixa specialists are active in various medical applications, as shown below.

Augmented reality in endoscopic surgery

For a leading manufacturer in endoscopy systems, Apixa developed real-time video enhancement for endoscopy imaging. Concretely, our specialists engineered an algorithm that distinguishes the imaging details in live video inside hollow organs and body cavities. They also created a reliable and ultrafast video transfer protocol to ensure minimum time delay between endoscopic surgeon action and live video feedback.

Deep learning evaluation of robotic surgery students

Evaluating surgeons in training during robot-driven surgery actions on dummy models is tedious and time consuming. Apixa currently contributes to a development project for a renowned surgical training center. Our specialists are developing software to automatically recognize and evaluate specific manual operations such as surgical clamping and sewing. Deep learning is integrated to insert a higher degree of surgical expertise in the visual evaluation process.

Automated analysis in nanofluidic processors

For a medical device manufacturer, APIXA developed software for generating computational microscopic video based on holographic imaging applied in nanofluidic blood sample processors. In addition, an algorithm was engineered to track and count red blood cells in the reconstructed video. Similarly, APIXA engineered deep learning based evaluation software to verify the correctness of nanofluidic phenomena in specific experiments. In terms of time efficiency, the automated evaluation replaces tens of persons performing manual evaluation full time.

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