A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation

BACKGROUND: Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring. METHODS: Using off-the-shelf wireless sensors we designed a prototype WBAN which features a standard ZigBee compliant radio and a common set of physiological, kinetic, and environmental sensors. RESULTS: We introduce a multi-tier telemedicine system and describe how we optimized our prototype WBAN implementation for computer-assisted physical rehabilitation applications and ambulatory monitoring. The system performs real-time analysis of sensors' data, provides guidance and feedback to the user, and can generate warnings based on the user's state, level of activity, and environmental conditions. In addition, all recorded information can be transferred to medical servers via the Internet and seamlessly integrated into the user's electronic medical record and research databases. CONCLUSION: WBANs promise inexpensive, unobtrusive, and unsupervised ambulatory monitoring during normal daily activities for prolonged periods of time. To make this technology ubiquitous and affordable, a number of challenging issues should be resolved, such as system design, configuration and customization, seamless integration, standardization, further utilization of common off-the-shelf components, security and privacy, and social issues

Enhancing Informative Frame Filtering by Water and Bubble Detection in Colonoscopy Videos

Colonoscopy has contributed to a marked decline in the number of colorectal cancer related deaths. However, recent data suggest that there is a significant (4-12%) miss-rate for the detection of even large polyps and cancers. To address this, we have been investigating an ‘automated feedback system’ which informs the endoscopist of possible sub-optimal inspection during colonoscopy. A fundamental step of this system is to distinguish non-informative frames from informative ones. Existing methods for this cannot classify water/bubble frames as non-informative even though they do not carry any useful visual information of the colon mucosa. In this paper, we propose a novel texture feature based on accumulation of pixel differences, which can detect water and bubble frames with very high accuracy with significantly less processing time. The experimental results show the proposed feature can achieve more than 93% overall accuracy in almost half of the processing time the existing methods take

Combining Science with Art to Educate and Motivate Patients Prior to Colorectal Cancer Screening

Colorectal cancer (CRC) is the second leading cause of cancer deaths in the US despite wide use of colonoscopy to prevent CRC and CRC-related mortality. Colonoscopy is used to identify and remove lesions that will lead to cancer, however, most deaths occur because lesions are not detected or completely removed during the procedure. Patients play a crucial role in the detection component of colonoscopy: the better the colon is prepared, the higher the chance of detection of all polyps and cancers. In general, patients are instructed to clean the colon by way of a paper or web-based form that lists the objective (scientific) steps involved; unfortunately this too often does not result in a well-prepared colon. Behavior is known to be heavily influenced by emotion. As the first phase of a smart education research project we created an artistic and instructional documentary in which patients engage with the educational content through emotional responses; i.e., we motivate patients to follow instructions by combining scientific with emotional aspects of CRC prevention including preparation of the colon prior to colonoscopy. In the second research phase we will test whether use of the documentary results in improved colon preparation

Multicenter Comparison of Molecular Tumor Boards in The Netherlands:Definition, Composition, Methods, and Targeted Therapy Recommendations

Background Molecular tumor boards (MTBs) provide rational, genomics-driven, patient-tailored treatment recommendations. Worldwide, MTBs differ in terms of scope, composition, methods, and recommendations. This study aimed to assess differences in methods and agreement in treatment recommendations among MTBs from tertiary cancer referral centers in The Netherlands. Materials and Methods MTBs from all tertiary cancer referral centers in The Netherlands were invited to participate. A survey assessing scope, value, logistics, composition, decision-making method, reporting, and registration of the MTBs was completed through on-site interviews with members from each MTB. Targeted therapy recommendations were compared using 10 anonymized cases. Participating MTBs were asked to provide a treatment recommendation in accordance with their own methods. Agreement was based on which molecular alteration(s) was considered actionable with the next line of targeted therapy. Results Interviews with 24 members of eight MTBs revealed that all participating MTBs focused on rare or complex mutational cancer profiles, operated independently of cancer type-specific multidisciplinary teams, and consisted of at least (thoracic and/or medical) oncologists, pathologists, and clinical scientists in molecular pathology. Differences were the types of cancer discussed and the methods used to achieve a recommendation. Nevertheless, agreement among MTB recommendations, based on identified actionable molecular alteration(s), was high for the 10 evaluated cases (86%). Conclusion MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational cancer profiles. We propose a "Dutch MTB model" for an optimal, collaborative, and nationally aligned MTB workflow. Implications for Practice Interpretation of genomic analyses for optimal choice of target therapy for patients with cancer is becoming increasingly complex. A molecular tumor board (MTB) supports oncologists in rationalizing therapy options. However, there is no consensus on the most optimal setup for an MTB, which can affect the quality of recommendations. This study reveals that the eight MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational profiles. The Dutch MTB model is based on a collaborative and nationally aligned workflow with interinstitutional collaboration and data sharing

Multicenter Comparison of Molecular Tumor Boards in The Netherlands: Definition, Composition, Methods, and Targeted Therapy Recommendations

Background: Molecular tumor boards (MTBs) provide rational, genomics-driven, patient-tailored treatment recommendations. Worldwide, MTBs differ in terms of scope, composition, methods, and recommendations. This study aimed to assess differences in methods and agreement in treatment recommendations among MTBs from tertiary cancer referral centers in The Netherlands. Materials and Methods: MTBs from all tertiary cancer referral centers in The Netherlands were invited to participate. A survey assessing scope, value, logistics, composition, decision-making method, reporting, and registration of the MTBs was completed through on-site interviews with members from each MTB. Targeted therapy recommendations were compared using 10 anonymized cases. Participating MTBs were asked to provide a treatment recommendation in accordance with their own methods. Agreement was based on which molecular alteration(s) was considered actionable with the next line of targeted therapy. Results: Interviews with 24 members of eight MTBs revealed that all participating MTBs focused on rare or complex mutational cancer profiles, operated independently of cancer type–specific multidisciplinary teams, and consisted of at least (thoracic and/or medical) oncologists, pathologists, and clinical scientists in molecular pathology. Differences were the types of cancer discussed and the methods used to achieve a recommendation. Nevertheless, agreement among MTB recommendations, based on identified actionable molecular alteration(s), was high for the 10 evaluated cases (86%). Conclusion: MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational cancer profiles. We propose a “Dutch MTB model” for an optimal, collaborative, and nationally aligned MTB workflow. Implications for Practice: Interpretation of genomic analyses for optimal choice of target therapy for patients with cancer is becoming increasingly complex. A molecular tumor board (MTB) supports oncologists in rationalizing therapy options. However, there is no consensus on the most optimal setup for an MTB, which can affect the quality of recommendations. This study reveals that the eight MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational profiles. The Dutch MTB model is based on a collaborative and nationally aligned workflow with interinstitutional collaboration and data sharing

Content Based Image Retrieval Using Depth Maps for Colonoscopy Images

Content Based Image Retrieval (CBIR) finds similar images given a query image. Effective CBIR has been actively studied over several decades. For measuring image similarity, low-level visual features (i.e., color, shape, texture, and spatial layout), combination of low-level features, or Convolutional Neural Network (CNN) are typically used. However, a similarity measure based on these features is not effective for some type of images, for example, colonoscopy images captured from colonoscopy procedures. This is because the low-level visual features of these images are mostly very similar. We propose a new method to compare these images and find their similarity in terms of their surface topology. First, we generate a grey-scale depth map image for each image, then extract four straight lines from it. Each point in the four lines has a grey-scale value (depth) in its depth map. The similarity of the two images is measured by comparing the depth values on the four corresponding lines from the two images. We propose a function to compute a difference (distance) between two sets of four lines using Mean Absolute Error. The experiments based on synthetic and real colonoscopy images show that the proposed method is promising.This proceeding is published as Rahman, Md Marufi, JungHwan Oh, Wallapak Tavanapong, and Piet C. de Groen. "Content Based Image Retrieval Using Depth Maps for Colonoscopy Images." (2023). In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS, pages 301-308. DOI: 10.5220/0011749100003414. Posted with permission.Under CC license (CC BY-NC-ND 4.0)

Automated Bite-block Detection to Distinguish Colonoscopy from Upper Endoscopy Using Deep Learning

This is a manuscript of a proceeding published as Rahman M.M., Oh J., Tavanapong W., Wong J., de Groen P.C. (2021) Automated Bite-block Detection to Distinguish Colonoscopy from Upper Endoscopy Using Deep Learning. In: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science, vol 13018. Springer, Cham. doi:10.1007/978-3-030-90436-4_17.Colonoscopy is currently the gold standard procedure for colorectal cancer (CRC) screening. However, the dominant explanations for the continued incidence of CRC are endoscopist-related factors. To address this, we have been investigating an automated feedback system which measures quality of colonos-copy automatically to assist the endoscopist to improve the quality of the actual procedure being performed. One of the fundamental steps for the automated qual-ity feedback system is to distinguish a colonoscopy from an upper endoscopy since upper endoscopy and colonoscopy procedures are performed in the same room at different times, and it is necessary to distinguish the type of a procedure prior to execution of any quality measurement method to evaluate the procedure. In upper endoscopy, a bite-block is inserted for patient protection. By detecting this bite-block appearance, we can distinguish colonoscopy from upper endos-copy. However, there are various colors (i.e., blue, green, white, etc.) of bite-blocks. Our solution utilizes analyses of Hue and Saturation values and two Con-volutional Neural Networks (CNNs). One CNN detects image patches of a bite-block regardless of its colors. The other CNN detects image patches of the tongue. The experimental results show that the proposed solution is highly promising