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Cellular phone enabled non-invasive tissue classifier.

Laufer S, Rubinsky B - PLoS ONE (2009)

Bottom Line: The results of the tissue analysis were returned to the remote data measurement site.When used for the detection of malignant tumors, classifiers can be designed to produce false positives in order to ensure that no tumors will be missed.This mode of operation has applications in remote non-invasive tissue diagnostics in situ in the body, in combination with medical imaging, as well as in remote diagnostics of biopsy samples in vitro.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioengineering in the Service of Humanity and Society, School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel. Shlomi.laufer@mail.huji.ac.il

ABSTRACT
Cellular phone technology is emerging as an important tool in the effort to provide advanced medical care to the majority of the world population currently without access to such care. In this study, we show that non-invasive electrical measurements and the use of classifier software can be combined with cellular phone technology to produce inexpensive tissue characterization. This concept was demonstrated by the use of a Support Vector Machine (SVM) classifier to distinguish through the cellular phone between heart and kidney tissue via the non-invasive multi-frequency electrical measurements acquired around the tissues. After the measurements were performed at a remote site, the raw data were transmitted through the cellular phone to a central computational site and the classifier was applied to the raw data. The results of the tissue analysis were returned to the remote data measurement site. The classifiers correctly determined the tissue type with a specificity of over 90%. When used for the detection of malignant tumors, classifiers can be designed to produce false positives in order to ensure that no tumors will be missed. This mode of operation has applications in remote non-invasive tissue diagnostics in situ in the body, in combination with medical imaging, as well as in remote diagnostics of biopsy samples in vitro.

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Related in: MedlinePlus

Electrode combinations.Red electrodes are used for current injunction, and green electrodes are used for voltage measurement. The combinations can be divided into four groups: I) Combinations 1–4, adjacent electrodes; II) Combinations 5–7, only one electrode away; III) Combinations 8–10, two electrodes away; and IV) Combinations 11–12, three electrodes away.
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pone-0005178-g003: Electrode combinations.Red electrodes are used for current injunction, and green electrodes are used for voltage measurement. The combinations can be divided into four groups: I) Combinations 1–4, adjacent electrodes; II) Combinations 5–7, only one electrode away; III) Combinations 8–10, two electrodes away; and IV) Combinations 11–12, three electrodes away.

Mentions: All impedance measurements were performed using a custom-developed impedance analyzer embedded in a single Printed Circuit Board (PCB). The impedance analyzer architecture is described in [15]. A total of 11 different frequencies, ranging from 1 kHz to 400 kHz, were measured. Using a manual switchboard, 12 different electrode configurations were employed. In each configuration, four electrodes were used, with two opposite electrodes for current injection and two opposite electrodes for voltage measurement. The different configurations are depicted in Figure 3. The data from each configuration were collected for five seconds.


Cellular phone enabled non-invasive tissue classifier.

Laufer S, Rubinsky B - PLoS ONE (2009)

Electrode combinations.Red electrodes are used for current injunction, and green electrodes are used for voltage measurement. The combinations can be divided into four groups: I) Combinations 1–4, adjacent electrodes; II) Combinations 5–7, only one electrode away; III) Combinations 8–10, two electrodes away; and IV) Combinations 11–12, three electrodes away.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2664897&req=5

pone-0005178-g003: Electrode combinations.Red electrodes are used for current injunction, and green electrodes are used for voltage measurement. The combinations can be divided into four groups: I) Combinations 1–4, adjacent electrodes; II) Combinations 5–7, only one electrode away; III) Combinations 8–10, two electrodes away; and IV) Combinations 11–12, three electrodes away.
Mentions: All impedance measurements were performed using a custom-developed impedance analyzer embedded in a single Printed Circuit Board (PCB). The impedance analyzer architecture is described in [15]. A total of 11 different frequencies, ranging from 1 kHz to 400 kHz, were measured. Using a manual switchboard, 12 different electrode configurations were employed. In each configuration, four electrodes were used, with two opposite electrodes for current injection and two opposite electrodes for voltage measurement. The different configurations are depicted in Figure 3. The data from each configuration were collected for five seconds.

Bottom Line: The results of the tissue analysis were returned to the remote data measurement site.When used for the detection of malignant tumors, classifiers can be designed to produce false positives in order to ensure that no tumors will be missed.This mode of operation has applications in remote non-invasive tissue diagnostics in situ in the body, in combination with medical imaging, as well as in remote diagnostics of biopsy samples in vitro.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioengineering in the Service of Humanity and Society, School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel. Shlomi.laufer@mail.huji.ac.il

ABSTRACT
Cellular phone technology is emerging as an important tool in the effort to provide advanced medical care to the majority of the world population currently without access to such care. In this study, we show that non-invasive electrical measurements and the use of classifier software can be combined with cellular phone technology to produce inexpensive tissue characterization. This concept was demonstrated by the use of a Support Vector Machine (SVM) classifier to distinguish through the cellular phone between heart and kidney tissue via the non-invasive multi-frequency electrical measurements acquired around the tissues. After the measurements were performed at a remote site, the raw data were transmitted through the cellular phone to a central computational site and the classifier was applied to the raw data. The results of the tissue analysis were returned to the remote data measurement site. The classifiers correctly determined the tissue type with a specificity of over 90%. When used for the detection of malignant tumors, classifiers can be designed to produce false positives in order to ensure that no tumors will be missed. This mode of operation has applications in remote non-invasive tissue diagnostics in situ in the body, in combination with medical imaging, as well as in remote diagnostics of biopsy samples in vitro.

Show MeSH
Related in: MedlinePlus