Identifying key mechanisms leading to visual recognition errors for missed colorectal polyps using eye-tracking technology

BACKGROUND AND AIM

Lack of visual recognition of colorectal polyps may lead to interval cancers. The mechanisms contributing to perceptual variation, particularly for subtle and advanced colorectal neoplasia, have scarcely been investigated. We aimed to evaluate visual recognition errors and provide novel mechanistic insights.

METHODS

Eleven participants (seven trainees and four medical students) evaluated images from the UCL polyp perception dataset, containing 25 polyps, using eye-tracking equipment. Gaze errors were defined as those where the lesion was not observed according to eye-tracking technology. Cognitive errors occurred when lesions were observed but not recognized as polyps by participants. A video study was also performed including 39 subtle polyps, where polyp recognition performance was compared with a convolutional neural network.

RESULTS

Cognitive errors occurred more frequently than gaze errors overall (65.6%), with a significantly higher proportion in trainees (P = 0.0264). In the video validation, the convolutional neural network detected significantly more polyps than trainees and medical students, with per-polyp sensitivities of 79.5%, 30.0%, and 15.4%, respectively.

CONCLUSIONS

Cognitive errors were the most common reason for visual recognition errors. The impact of interventions such as artificial intelligence, particularly on different types of perceptual errors, needs further investigation including potential effects on learning curves. To facilitate future research, a publicly accessible visual perception colonoscopy polyp database was created.

A Continuum Robot and Control Interface for Surgical Assist in Fetoscopic Interventions

Twin–twin transfusion syndrome requires interventional treatment using a fetoscopically introduced laser to sever the shared blood supply between the fetuses. This is a delicate procedure relying on small instrumentation with limited articulation to guide the laser tip and a narrow field of view to visualize all relevant vascular connections. In this letter, we report on a mechatronic design for a comanipulated instrument that combines concentric tube actuation to a larger manipulator constrained by a remote centre of motion. A stereoscopic camera is mounted at the distal tip and used for imaging. Our mechanism provides enhanced dexterity and stability of the imaging device. We demonstrate that the imaging system can be used for computing geometry and enhancing the view at the operating site. Results using electromagnetic sensors for verification and comparison to visual odometry from the distal sensor show that our system is promising and can be developed further for multiple clinical needs in fetoscopic procedures.

Enhanced imaging colonoscopy facilitates dense motion-based 3D reconstruction

We propose a novel approach for estimating a dense 3D model of neoplasia in colonoscopy using enhanced imaging endoscopy modalities. Estimating a dense 3D model of neoplasia is important to make 3D measurements and to classify the superficial lesions in standard frameworks such as the Paris classification. However, it is challenging to obtain decent dense 3D models using computer vision techniques such as Structure-from-Motion due to the lack of texture in conventional (white light) colonoscopy. Therefore, we propose to use enhanced imaging endoscopy modalities such as Narrow Band Imaging and chromoendoscopy to facilitate the 3D reconstruction process. Thanks to the use of these enhanced endoscopy techniques, visualization is improved, resulting in more reliable feature tracks and 3D reconstruction results. We first build a sparse 3D model of neoplasia using Structure-from-Motion from enhanced endoscopy imagery. Then, the sparse reconstruction is densified using a Multi-View Stereo approach, and finally the dense 3D point cloud is transformed into a mesh by means of Poisson surface reconstruction. The obtained dense 3D models facilitate classification of neoplasia in the Paris classification, in which the 3D size and the shape of the neoplasia play a major role in the diagnosis.

How big is this neoplasia? live colonoscopic size measurement using the Infocus-Breakpoint

Colonoscopy is the reference medical examination for early diagnosis and treatment of colonic diseases. This minimally invasive technique allows endoscopists to explore the colon cavity and remove neoplasias - abnormal growths of tissue - which may develop into malignant tumors. The size, shape and appearance of a neoplasia are essential cues for diagnostic. However, the size is difficult to estimate because the absolute scale of the observed tissue is not directly conveyed in the 2D colonoscopic images. An erroneous size estimate may lead to inappropriate treatment. There currently exist no solutions to reproducible neoplasia size measurement adapted to colonoscopy. We propose a colonoscopic size measurement system for neoplasias. By using a simple planar geometry, the key technical problem is reduced to resolving scale. Our core contribution is introducing the Infocus-Breakpoint (IB) that allows us to resolve scale from a regular colonoscopic video. We define the IB as the lower limit of the colonoscope's depth of field. The IB corresponds to a precise colonoscope to tissue distance, called the reference depth, which we calibrate preoperatively. We detect the IB intraoperatively thanks to two novel modules: deformable Blur-Estimating Tracking (BET) and Blur-Model Fitting (BMF). With our system, the endoscopist may interactively measure the length and area of a neoplasia in a 2D colonoscopic image directly. Our system needs no hardware modification to standard monocular colonoscopes, yet reaching a size measurement accuracy of the order of a millimeter, as shown on several phantom and patient datasets.