Unsupervised Learning‐Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone
Objectives To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health. Methods This...
Gespeichert in:
| Veröffentlicht in: | Journal of ultrasound in medicine 2024-09, Vol.43 (9), p.1711-1722 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1722 |
|---|---|
| container_issue | 9 |
| container_start_page | 1711 |
| container_title | Journal of ultrasound in medicine |
| container_volume | 43 |
| creator | Li, Qing Tran, Tho N. H. T. Guo, Jialin Li, Boyi Xu, Kailiang Le, Lawrence H. Ta, Dean |
| description | Objectives
To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health.
Methods
This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C‐means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators.
Results
The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value |
| doi_str_mv | 10.1002/jum.16505 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_3068759017</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3068759017</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2855-a70c3841bf029598bf8b0471d7970510daebf30c350d7f1b98e7154a973340b33</originalsourceid><addsrcrecordid>eNp10L1OwzAUhmELgaD8DNwAyghD2uM4rp0RKn5VYKGskZOcIKPELnYCdOMSuEauBJcWNiZL1qNPRy8hhxSGFCAZPfftkI458A0yoJxDnI0p2yQDSISM0yQTO2TX--dAgYp0m-wwKQUTkAxINTO-n6N71R6raIrKGW2evj4-z9Ty4xaV7x22aLrI1tGs6Zzy1ugyOn3XqokenDK-1d5ra6JHbGypu0WkTXSH1qguiDNrcJ9s1arxeLB-98js4vxhchVP7y-vJ6fTuEwk57ESUDKZ0qKGJOOZLGpZQCpoJTIBnEKlsKhZMBwqUdMikygoT1UmGEuhYGyPHK92586-9Oi7PJxWYtMog7b3OYOxFDwLEQI9WdHSWe8d1vnc6Va5RU4hX0bNQ9T8J2qwR-vZvmix-pO_FQMYrcCbbnDx_1J-M7tdTX4DU8yCDQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3068759017</pqid></control><display><type>article</type><title>Unsupervised Learning‐Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Li, Qing ; Tran, Tho N. H. T. ; Guo, Jialin ; Li, Boyi ; Xu, Kailiang ; Le, Lawrence H. ; Ta, Dean</creator><creatorcontrib>Li, Qing ; Tran, Tho N. H. T. ; Guo, Jialin ; Li, Boyi ; Xu, Kailiang ; Le, Lawrence H. ; Ta, Dean</creatorcontrib><description>Objectives
To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health.
Methods
This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C‐means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators.
Results
The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value <.001), body length (ρ = 0.583, P value <.001), and gestational age (ρ = 0.557, P value <.001).
Conclusion
These findings suggest that fuzzy C‐means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders.</description><identifier>ISSN: 0278-4297</identifier><identifier>ISSN: 1550-9613</identifier><identifier>EISSN: 1550-9613</identifier><identifier>DOI: 10.1002/jum.16505</identifier><identifier>PMID: 38873702</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Algorithms ; axial transmission ; bone quantitative ultrasound ; Female ; fuzzy C‐means clustering ; Humans ; Infant, Newborn ; Male ; Phantoms, Imaging ; ReliefF algorithm ; Reproducibility of Results ; speed of sound ; Tibia - diagnostic imaging ; Tibia - physiology ; Ultrasonography - methods ; Unsupervised Machine Learning</subject><ispartof>Journal of ultrasound in medicine, 2024-09, Vol.43 (9), p.1711-1722</ispartof><rights>2024 American Institute of Ultrasound in Medicine.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2855-a70c3841bf029598bf8b0471d7970510daebf30c350d7f1b98e7154a973340b33</cites><orcidid>0000-0002-1269-1166</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjum.16505$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjum.16505$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38873702$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Qing</creatorcontrib><creatorcontrib>Tran, Tho N. H. T.</creatorcontrib><creatorcontrib>Guo, Jialin</creatorcontrib><creatorcontrib>Li, Boyi</creatorcontrib><creatorcontrib>Xu, Kailiang</creatorcontrib><creatorcontrib>Le, Lawrence H.</creatorcontrib><creatorcontrib>Ta, Dean</creatorcontrib><title>Unsupervised Learning‐Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone</title><title>Journal of ultrasound in medicine</title><addtitle>J Ultrasound Med</addtitle><description>Objectives
To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health.
Methods
This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C‐means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators.
Results
The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value <.001), body length (ρ = 0.583, P value <.001), and gestational age (ρ = 0.557, P value <.001).
Conclusion
These findings suggest that fuzzy C‐means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders.</description><subject>Algorithms</subject><subject>axial transmission</subject><subject>bone quantitative ultrasound</subject><subject>Female</subject><subject>fuzzy C‐means clustering</subject><subject>Humans</subject><subject>Infant, Newborn</subject><subject>Male</subject><subject>Phantoms, Imaging</subject><subject>ReliefF algorithm</subject><subject>Reproducibility of Results</subject><subject>speed of sound</subject><subject>Tibia - diagnostic imaging</subject><subject>Tibia - physiology</subject><subject>Ultrasonography - methods</subject><subject>Unsupervised Machine Learning</subject><issn>0278-4297</issn><issn>1550-9613</issn><issn>1550-9613</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10L1OwzAUhmELgaD8DNwAyghD2uM4rp0RKn5VYKGskZOcIKPELnYCdOMSuEauBJcWNiZL1qNPRy8hhxSGFCAZPfftkI458A0yoJxDnI0p2yQDSISM0yQTO2TX--dAgYp0m-wwKQUTkAxINTO-n6N71R6raIrKGW2evj4-z9Ty4xaV7x22aLrI1tGs6Zzy1ugyOn3XqokenDK-1d5ra6JHbGypu0WkTXSH1qguiDNrcJ9s1arxeLB-98js4vxhchVP7y-vJ6fTuEwk57ESUDKZ0qKGJOOZLGpZQCpoJTIBnEKlsKhZMBwqUdMikygoT1UmGEuhYGyPHK92586-9Oi7PJxWYtMog7b3OYOxFDwLEQI9WdHSWe8d1vnc6Va5RU4hX0bNQ9T8J2qwR-vZvmix-pO_FQMYrcCbbnDx_1J-M7tdTX4DU8yCDQ</recordid><startdate>202409</startdate><enddate>202409</enddate><creator>Li, Qing</creator><creator>Tran, Tho N. H. T.</creator><creator>Guo, Jialin</creator><creator>Li, Boyi</creator><creator>Xu, Kailiang</creator><creator>Le, Lawrence H.</creator><creator>Ta, Dean</creator><general>John Wiley & Sons, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-1269-1166</orcidid></search><sort><creationdate>202409</creationdate><title>Unsupervised Learning‐Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone</title><author>Li, Qing ; Tran, Tho N. H. T. ; Guo, Jialin ; Li, Boyi ; Xu, Kailiang ; Le, Lawrence H. ; Ta, Dean</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2855-a70c3841bf029598bf8b0471d7970510daebf30c350d7f1b98e7154a973340b33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>axial transmission</topic><topic>bone quantitative ultrasound</topic><topic>Female</topic><topic>fuzzy C‐means clustering</topic><topic>Humans</topic><topic>Infant, Newborn</topic><topic>Male</topic><topic>Phantoms, Imaging</topic><topic>ReliefF algorithm</topic><topic>Reproducibility of Results</topic><topic>speed of sound</topic><topic>Tibia - diagnostic imaging</topic><topic>Tibia - physiology</topic><topic>Ultrasonography - methods</topic><topic>Unsupervised Machine Learning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Qing</creatorcontrib><creatorcontrib>Tran, Tho N. H. T.</creatorcontrib><creatorcontrib>Guo, Jialin</creatorcontrib><creatorcontrib>Li, Boyi</creatorcontrib><creatorcontrib>Xu, Kailiang</creatorcontrib><creatorcontrib>Le, Lawrence H.</creatorcontrib><creatorcontrib>Ta, Dean</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of ultrasound in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Qing</au><au>Tran, Tho N. H. T.</au><au>Guo, Jialin</au><au>Li, Boyi</au><au>Xu, Kailiang</au><au>Le, Lawrence H.</au><au>Ta, Dean</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unsupervised Learning‐Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone</atitle><jtitle>Journal of ultrasound in medicine</jtitle><addtitle>J Ultrasound Med</addtitle><date>2024-09</date><risdate>2024</risdate><volume>43</volume><issue>9</issue><spage>1711</spage><epage>1722</epage><pages>1711-1722</pages><issn>0278-4297</issn><issn>1550-9613</issn><eissn>1550-9613</eissn><abstract>Objectives
To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health.
Methods
This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C‐means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators.
Results
The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value <.001), body length (ρ = 0.583, P value <.001), and gestational age (ρ = 0.557, P value <.001).
Conclusion
These findings suggest that fuzzy C‐means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>38873702</pmid><doi>10.1002/jum.16505</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-1269-1166</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0278-4297 |
| ispartof | Journal of ultrasound in medicine, 2024-09, Vol.43 (9), p.1711-1722 |
| issn | 0278-4297 1550-9613 1550-9613 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_3068759017 |
| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms axial transmission bone quantitative ultrasound Female fuzzy C‐means clustering Humans Infant, Newborn Male Phantoms, Imaging ReliefF algorithm Reproducibility of Results speed of sound Tibia - diagnostic imaging Tibia - physiology Ultrasonography - methods Unsupervised Machine Learning |
| title | Unsupervised Learning‐Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T16%3A52%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Unsupervised%20Learning%E2%80%90Based%20Measurement%20of%20Ultrasonic%20Axial%20Transmission%20Velocity%20in%20Neonatal%20Bone&rft.jtitle=Journal%20of%20ultrasound%20in%20medicine&rft.au=Li,%20Qing&rft.date=2024-09&rft.volume=43&rft.issue=9&rft.spage=1711&rft.epage=1722&rft.pages=1711-1722&rft.issn=0278-4297&rft.eissn=1550-9613&rft_id=info:doi/10.1002/jum.16505&rft_dat=%3Cproquest_cross%3E3068759017%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3068759017&rft_id=info:pmid/38873702&rfr_iscdi=true |