A 3D machine vision method for non-invasive assessment of respiratory function

Smith, Lyndon; Smith, Melvyn L.; Fletcher, Margaret; Henderson, John

doi:10.1002/rcs.1669

Cited by 11 publications

(6 citation statements)

References 20 publications

(27 reference statements)

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“…An important trend in modern medicine is to develop less-invasive diagnostic and therapeutic techniques that can replace invasive procedures, while maintaining high accuracy and efficacy. 1,2 In addition, patients expect to be involved, and that their preferences be considered. 3 The use of digital systems in clinical practice allows data collection, computer analysis, and machine learning as well as development of algorithms that were not possible in the past.…”

Section: Introductionmentioning

confidence: 99%

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

et al. 2021

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We present a noninvasive Web-based app to help exclude or diagnose myelodysplastic syndrome (MDS), a bone marrow (BM) disorder with cytopenias and leukemic risk, diagnosed by BM examination. A sample of 502 MDS patients from the European MDS (EUMDS) registry (n > 2600) was combined with 502 controls (all BM proven). Gradient-boosted models (GBMs) were used to predict/exclude MDS using demographic, clinical, and laboratory variables. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models, and performance was validated using 100 times fivefold cross-validation. Model stability was assessed by repeating its fit using different randomly chosen groups of 502 EUMDS cases. AUC was 0.96 (95% confidence interval, 0.95-0.97). MDS is predicted/excluded accurately in 86% of patients with unexplained anemia. A GBM score (range, 0-1) of less than 0.68 (GBM < 0.68) resulted in a negative predictive value of 0.94, that is, MDS was excluded. GBM ≥ 0.82 provided a positive predictive value of 0.88, that is, MDS. The diagnosis of the remaining patients (0.68 ≤ GBM < 0.82) is indeterminate. The discriminating variables: age, sex, hemoglobin, white blood cells, platelets, mean corpuscular volume, neutrophils, monocytes, glucose, and creatinine. A Web-based app was developed; physicians could use it to exclude or predict MDS noninvasively in most patients without a BM examination. Future work will add peripheral blood cytogenetics/genetics, EUMDS-based prospective validation, and prognostication.

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Section: Introductionmentioning

confidence: 99%

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

et al. 2021

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“…This would be expected to assist with early identification of the condition, enabling optimal dispensation of drugs or other therapy for appropriate and tailored treatment. These considerations led to the development in CMV of an optical system that employs a technique designated as ‘Novel non‐invasive assessment of respiratory function’ (NORM) . NORM also employs PS for 3D surface recovery , but on a larger scale, and here the recovery is continuous and in real time (i.e.…”

Section: Methodology and Resultsmentioning

confidence: 99%

“…A number of conditions have already been addressed by research in CMV that has confirmed the utility of high resolution 3D machine vision for generating clinically useful data. Example conditions, and their associated medical vision devices are as follows: skin cancer (the SA) , respiratory illnesses and scoliosis (the NORM system) , plagiocephaly (PLAGIO – here both GS and PS were employed) , and 3D expression analysis (the UWE 4D PS System) . The latter enables real‐time analysis of 3D changes in facial expression (e.g.…”

Section: Future Workmentioning

confidence: 99%

Realistic and interactive high‐resolution 4D environments for real‐time surgeon and patient interaction

Smith

Farooq

Smith

et al. 2016

Robotics Computer Surgery

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Background Remote consultations that are realistic enough to be useful medically offer considerable clinical, logistical and cost benefits. Despite advances in virtual reality and vision hardware and software, these benefits are currently often unrealised. Method The proposed approach combines high spatial and temporal resolution 3D and 2D machine vision with virtual reality techniques, in order to develop new environments and instruments that will enable realistic remote consultations and the generation of new types of useful clinical data. Results New types of clinical data have been generated for skin analysis and respiration measurement; and the combination of 3D with 2D data was found to offer potential for the generation of realistic virtual consultations. Conclusion An innovative combination of high resolution machine vision data and virtual reality online methods, promises to provide advanced functionality and significant medical benefits, particularly in regions where populations are dispersed or access to clinicians is limited. Copyright © 2016 John Wiley & Sons, Ltd.

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“…After the server receives the transmission request, it establishes a connection with the client for image transmission. 23 After the image transmission is completed, the server performs human target detection on the image and pushes the results to the client. The client prioritizes the detection results and displays them in the monitoring screen in real time.…”

Section: Function Testmentioning

confidence: 99%

(Retracted) Real-time detection of panoramic multitargets based on machine vision and deep learning

2022

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. With the continuous development of computer vision technology, moving object detection technology has been paid enough attention and made great progress. Many new methods and new equipment have been developed. As an important part of computer vision, it has important applications in battlefield reconnaissance, video surveillance, image compression and retrieval, human–computer interaction and other research fields, moving object detection and tracking algorithm has always been a research hotspot. We mainly study the panoramic multitarget real-time detection based on machine vision and deep learning. By studying the principle of multitarget real-time detection based on machine vision and deep learning, the panoramic multitarget real-time detection model based on machine vision and deep learning is determined. The principle and correction effect of existing image distortion correction algorithm are analyzed, and the existing problems are summarized. Aiming at the problem that the existing image distortion correction effect is not good, a new method based on machine vision and deep learning is proposed. A real-time panoramic multitarget detection method based on degree learning is proposed. The experimental results show that when the target is moving at medium speed and slow speed, the success rate of tracking is 97% and 95%, respectively; the probability of successful target detection is 100% and 97%, respectively. Experimental results show that the improved method can solve the problem of particle degradation and improve the accuracy of real-time detection.

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A 3D machine vision method for non-invasive assessment of respiratory function

Cited by 11 publications

References 20 publications

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

Realistic and interactive high‐resolution 4D environments for real‐time surgeon and patient interaction

(Retracted) Real-time detection of panoramic multitargets based on machine vision and deep learning

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