Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Efficacy and safety of PAC-14028 cream - a novel, topical, nonsteroidal, selective TRPV1 antagonist in patients with mild-to-moderate atopic dermatitis: a phase IIb randomized trial.

The British journal of dermatology·2019
Same author

Tumor size and elasticity estimation using Smartphone-based Compression-Induced scope.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2017
Same author

Difference of achalasia subtypes based on clinical symptoms, radiographic findings, and stasis scores.

Revista de gastroenterologia de Mexico (English)·2017
Same author

Search for displaced supersymmetry in events with an electron and a muon with large impact parameters.

Physical review letters·2015
Same author

Study of vector boson scattering and search for new physics in events with two same-sign leptons and two jets.

Physical review letters·2015
Same author

Measurement of the forward-backward asymmetry in the production of B(±) mesons in pp̄ collisions at √s=1.96  TeV.

Physical review letters·2015

Related Experiment Video

Updated: Dec 6, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.9K

Breast Tumor Malignancy Classification using Smartphone Compression-induced Sensing System and Deformation Index

S Choi, V Oleksyuk, D Caroline

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a portable, non-invasive tool that uses smartphone cameras to detect breast tumors by analyzing how tissue deforms under pressure. By measuring light diffusion patterns and calculating a specific deformation ratio, the system aims to distinguish between benign and malignant growths. Early testing shows promise for accessible, low-cost cancer screening.

    Keywords:
    mobile health diagnosticstissue deformation imagingpoint-of-care oncologylight diffusion analysis

    Frequently Asked Questions

    More Related Videos

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.4K
    A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
    10:37

    A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

    Published on: August 22, 2025

    892

    Related Experiment Videos

    Last Updated: Dec 6, 2025

    Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
    15:48

    Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

    Published on: December 15, 2014

    22.9K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.4K
    A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
    10:37

    A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

    Published on: August 22, 2025

    892

    Area of Science:

    • Biomedical engineering research within smartphone-based compression-induced sensing systems
    • Oncology diagnostics and medical imaging technology

    Background:

    Current clinical breast cancer screening methods often require expensive, stationary equipment that limits access in resource-constrained settings. Portable diagnostic tools remain a significant challenge for early detection in remote areas. No prior work had resolved the integration of smartphone hardware with tissue-specific mechanical sensing. That uncertainty drove the development of a mobile-based platform for non-invasive tumor characterization. Prior research has shown that light diffusion patterns can reflect underlying tissue properties during physical stress. However, existing systems lack the portability required for widespread, point-of-care implementation. This gap motivated the creation of a sensing system that utilizes standard mobile devices for image acquisition. The field requires accessible solutions to improve screening rates and patient outcomes globally.

    Purpose Of The Study:

    The authors aimed to develop a non-invasive method for characterizing early-stage breast tumors using smartphone technology. They sought to address the lack of portable diagnostic tools for breast cancer screening. The researchers hypothesized that light diffusion patterns could reveal mechanical properties of tissue under compression. They intended to create a system that integrates mobile hardware with cloud-based data processing. The study focused on estimating tumor size and classifying malignancy through tactile image analysis. They aimed to provide a low-cost alternative to traditional imaging modalities. The team wanted to test the feasibility of using a deformation index ratio for clinical assessment. This work addresses the need for accessible, point-of-care solutions in oncology diagnostics.

    Main Methods:

    The researchers designed a mobile-based platform to capture and process tissue deformation images. Their review approach involved evaluating a cohort of seven patients for feasibility testing. They utilized light diffusion patterns to observe how breast tissue reacts to external force. The team implemented a cloud-based architecture to facilitate image transfer and user interaction. They applied projection analysis to estimate the physical dimensions of the identified masses. The investigators calculated a deformation index ratio to quantify the mechanical differences between healthy and affected regions. This metric relied on comparing the light patterns across distinct tissue zones. The study focused on validating the diagnostic potential of this portable, non-invasive hardware configuration.

    Main Results:

    The system achieved a sensitivity of 67.0% for identifying malignant tumors in the feasibility cohort. The researchers observed 100% specificity during these initial diagnostic tests. Regarding dimensional accuracy, the projection analysis resulted in an average error of 52.3% for tumor size estimation. These values reflect the performance of the smartphone-based sensing platform under controlled conditions. The data indicate that the deformation index ratio effectively distinguishes between different tissue types. The findings highlight the potential of using light diffusion to characterize tumor hardness. The study provides quantitative evidence for the feasibility of this mobile diagnostic approach. These results represent the first assessment of the system's accuracy in a clinical setting.

    Conclusions:

    The authors propose that their smartphone-based system offers a viable pathway for non-invasive breast tumor screening. This approach demonstrates that light diffusion patterns can effectively capture mechanical tissue properties. The researchers report a sensitivity of 67.0% for malignancy classification in their initial feasibility cohort. They also observed 100% specificity during these preliminary tests. These findings suggest that the deformation index ratio serves as a useful metric for distinguishing tumor types. The team notes that tumor size estimation currently carries an average error of 52.3%. Future iterations will focus on refining the projection analysis to improve dimensional accuracy. The study provides a foundation for developing low-cost, mobile-integrated diagnostic tools for clinical use.

    The researchers propose a method where the system calculates a deformation index ratio by comparing light diffusion changes in healthy versus tumorous tissue. This mechanical response, captured via smartphone imaging during compression, allows the device to differentiate between benign and malignant breast lesions.

    The platform utilizes a smartphone camera to record light diffusion patterns, while a cloud-based interface handles data transfer and processing. This dual-component architecture enables the capture of tactile images resulting from the size and hardness of the compressed tissue.

    The authors state that the system requires controlled compression of the breast tissue to generate the necessary deformation patterns. This physical stress is essential because it creates the specific tactile images needed for the projection analysis and the subsequent calculation of the deformation index ratio.

    Projection analysis serves as the primary data processing technique to estimate the physical dimensions of the tumor. By analyzing the light diffusion patterns within the captured images, the software calculates the size of the growth, although current results show a 52.3% average error.

    The researchers measured the system's diagnostic performance using a small feasibility group of seven patients. They reported a sensitivity of 67.0% and a specificity of 100% when using the deformation index ratio to classify the malignancy of the detected tumors.

    The authors suggest that their mobile-integrated platform could improve access to early-stage screening. They imply that by leveraging existing smartphone technology, this approach may provide a low-cost, non-invasive alternative to traditional clinical imaging for detecting breast abnormalities.