Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots

In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.Comment: submitted to IROS 201

A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is considered as a minimally invasive novel diagnostic technology to inspect the entire GI tract and to diagnose various diseases and pathologies. Since the development of this technology, medical device companies and many groups have made significant progress to turn such passive capsule endoscopes into robotic active capsule endoscopes to achieve almost all functions of current active flexible endoscopes. However, the use of robotic capsule endoscopy still has some challenges. One such challenge is the precise localization of such active devices in 3D world, which is essential for a precise three-dimensional (3D) mapping of the inner organ. A reliable 3D map of the explored inner organ could assist the doctors to make more intuitive and correct diagnosis. In this paper, we propose to our knowledge for the first time in literature a visual simultaneous localization and mapping (SLAM) method specifically developed for endoscopic capsule robots. The proposed RGB-Depth SLAM method is capable of capturing comprehensive dense globally consistent surfel-based maps of the inner organs explored by an endoscopic capsule robot in real time. This is achieved by using dense frame-to-model camera tracking and windowed surfelbased fusion coupled with frequent model refinement through non-rigid surface deformations

Thermomagnetic-Responsive Self-Folding Microgrippers for Improving Minimally Invasive Surgical Techniques and Biopsies

Traditional open surgery complications are typically due to trauma caused by accessing the procedural site rather than the procedure itself. Minimally invasive surgery allows for fewer complications as microdevices operate through small incisions or natural orifices. However, current minimally invasive tools typically have restricted maneuverability, accessibility, and positional control of microdevices. Thermomagnetic-responsive microgrippers are microscopic multi-fingered devices that respond to temperature changes due to the presence of thermal-responsive polymers. Polymeric devices, made of poly(N-isopropylacrylamide-co-acrylic acid) (pNIPAM-AAc) and polypropylene fumarate (PPF), self-fold due to swelling and contracting of the hydrogel layer. In comparison, soft metallic devices feature a pre-stressed metal bilayer and polymer hinges that soften with increased temperature. Both types of microdevices can self-actuate when exposed to the elevated temperature of a cancerous tumor region, allowing for direct targeting for biopsies. Microgrippers can also be doped to become magnetically responsive, allowing for direction without tethers and the retrieval of microdevices containing excised tissue. The smaller size of stimuli-responsive microgrippers allows for their movement through hard-to-reach areas within the body and the successful extraction of intact cells, RNA and DNA. This review discusses the mechanisms of thermal- and magnetic-responsive microdevices and recent advances in microgripper technology to improve minimally invasive surgical techniques

Capsule endoscopy of the future: What's on the horizon?

Capsule endoscopes have evolved from passively moving diagnostic devices to actively moving systems with potential therapeutic capability. In this review, we will discuss the state of the art, define the current shortcomings of capsule endoscopy, and address research areas that aim to overcome said shortcomings. Developments in capsule mobility schemes are emphasized in this text, with magnetic actuation being the most promising endeavor. Research groups are working to integrate sensor data and fuse it with robotic control to outperform today's standard invasive procedures, but in a less intrusive manner. With recent advances in areas such as mobility, drug delivery, and therapeutics, we foresee a translation of interventional capsule technology from the bench-top to the clinical setting within the next 10 years

Advanced medical micro-robotics for early diagnosis and therapeutic interventions

Recent technological advances in micro-robotics have demonstrated their immense potential for biomedical applications. Emerging micro-robots have versatile sensing systems, flexible locomotion and dexterous manipulation capabilities that can significantly contribute to the healthcare system. Despite the appreciated and tangible benefits of medical micro-robotics, many challenges still remain. Here, we review the major challenges, current trends and significant achievements for developing versatile and intelligent micro-robotics with a focus on applications in early diagnosis and therapeutic interventions. We also consider some recent emerging micro-robotic technologies that employ synthetic biology to support a new generation of living micro-robots. We expect to inspire future development of micro-robots toward clinical translation by identifying the roadblocks that need to be overcome

The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

Capsule endoscopy has revolutionized the management of small-bowel diseases owing to its convenience and noninvasiveness. Capsule endoscopy is a common method for the evaluation of obscure gastrointestinal bleeding, Crohn’s disease, small-bowel tumors, and polyposis syndrome. However, the laborious reading process, oversight of small-bowel lesions, and lack of locomotion are major obstacles to expanding its application. Along with recent advances in artificial intelligence, several studies have reported the promising performance of convolutional neural network systems for the diagnosis of various small-bowel lesions including erosion/ulcers, angioectasias, polyps, and bleeding lesions, which have reduced the time needed for capsule endoscopy interpretation. Furthermore, colon capsule endoscopy and capsule endoscopy locomotion driven by magnetic force have been investigated for clinical application, and various capsule endoscopy prototypes for active locomotion, biopsy, or therapeutic approaches have been introduced. In this review, we will discuss the recent advancements in artificial intelligence in the field of capsule endoscopy, as well as studies on other technological improvements in capsule endoscopy