Maturity assessment of software-driven medical technologies: a quantitative score derived from a quality standard for the research phase.

Quantitatively assessing the level of readiness of medical technology improves its chance of successfully transfer from research to industry but remains a challenge. As many innovative medical devices are associated with or incorporate software, this article presents a methodology for evaluating the software maturity of a "Software-driven Medical Technology" (SdMT) during the research phase. A technological maturity model is developed by methodologically extracting relevant terms from the ISO/IEC 62304 standard, the main industry standard for medical device software, and results in a list of required software engineering artifacts. This list and the relative weight of the artifacts are used to establish a software maturity score for SdMT and the corresponding assessment questionnaire. The consistency of the model is demonstrated by analyzing the obtained score system relatively with the standard. The maturity score of a SdMT can be assessed during the research phase and depends on the number and importance of the artifacts already present at the time of evaluation.Clinical relevance- The proposed quantitative maturity score can help the medical technology innovation actors (clinicians, researchers and industrials) to better identify, improve and fasten the readiness of technology for clinical investigation and technology transfer.

Qualitative Comparison of Image Stitching Algorithms for Multi-Camera Systems in Laparoscopy.

Multi-camera systems were recently introduced into laparoscopy to increase the narrow field of view of the surgeon. The video streams are stitched together to create a panorama that is easier for the surgeon to comprehend. Multi-camera prototypes for laparoscopy use quite basic algorithms and have only been evaluated on simple laparoscopic scenarios. The more recent state-of-the-art algorithms, mainly designed for the smartphone industry, have not yet been evaluated in laparoscopic conditions. We developed a simulated environment to generate a dataset of multi-view images displaying a wide range of laparoscopic situations, which is adaptable to any multi-camera system. We evaluated classical and state-of-the-art image stitching techniques used in non-medical applications on this dataset, including one unsupervised deep learning approach. We show that classical techniques that use global homography fail to provide a clinically satisfactory rendering and that even the most recent techniques, despite providing high quality panorama images in non-medical situations, may suffer from poor alignment or severe distortions in simulated laparoscopic scenarios. We highlight the main advantages and flaws of each algorithm within a laparoscopic context, identify the main remaining challenges that are specific to laparoscopy, and propose methods to improve these approaches. We provide public access to the simulated environment and dataset.