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Buswinka CJ, Rosenberg DB, Simikyan RG, Osgood RT, Fernandez K, Nitta H, Hayashi Y, Liberman LW, Nguyen E, Yildiz E, Kim J, Jarysta A, Renauld J, Wesson E, Wang H, Thapa P, Bordiga P, McMurtry N, Llamas J, Kitcher SR, López-Porras AI, Cui R, Behnammanesh G, Bird JE, Ballesteros A, Vélez-Ortega AC, Edge ASB, Deans MR, Gnedeva K, Shrestha BR, Manor U, Zhao B, Ricci AJ, Tarchini B, Basch ML, Stepanyan R, Landegger LD, Rutherford MA, Liberman MC, Walters BJ, Kros CJ, Richardson GP, Cunningham LL, Indzhykulian AA. Large-scale annotated dataset for cochlear hair cell detection and classification. Sci Data 2024; 11:416. [PMID: 38653806 PMCID: PMC11039649 DOI: 10.1038/s41597-024-03218-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 04/03/2024] [Indexed: 04/25/2024] Open
Abstract
Our sense of hearing is mediated by cochlear hair cells, of which there are two types organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains 5-15 thousand terminally differentiated hair cells, and their survival is essential for hearing as they do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. Machine learning can be used to automate the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, rat, guinea pig, pig, primate, and human cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 107,000 hair cells which have been identified and annotated as either inner or outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair-cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to give other hearing research groups the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.
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Affiliation(s)
- Christopher J Buswinka
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
- Speech and Hearing Biosciences and Technology graduate program, Harvard University, Cambridge, MA, 02138, USA
| | - David B Rosenberg
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
- Department of Cell and Developmental Biology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Rubina G Simikyan
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
| | - Richard T Osgood
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
- Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, UK
| | - Katharine Fernandez
- Section on Sensory Cell Biology, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Hidetomi Nitta
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
| | - Yushi Hayashi
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
| | - Leslie W Liberman
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
| | - Emily Nguyen
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
| | - Erdem Yildiz
- Department of Otolaryngology, Head and Neck Surgery, Vienna General Hospital and Medical University of Vienna, 1090, Vienna, Austria
| | - Jinkyung Kim
- Department of Otolaryngology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Otolaryngology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - Justine Renauld
- Department of Otolaryngology-Head and Neck Surgery, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Ella Wesson
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
| | - Haobing Wang
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
| | - Punam Thapa
- The University of Mississippi Medical Center, Department of Otolaryngology - Head and Neck Surgery, Jackson, MS, 39216, USA
| | - Pierrick Bordiga
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
| | - Noah McMurtry
- Department of Otolaryngology-Head and Neck Surgery, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Juan Llamas
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, 90033, USA
- Tina and Rick Caruso Department of Otolaryngology-Head and Neck Surgery, University of Southern California, Los Angeles, CA, 90033, USA
| | - Siân R Kitcher
- Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, UK
| | - Ana I López-Porras
- Department of Physiology, University of Kentucky, Lexington, KY, 40536, USA
| | - Runjia Cui
- Section on Sensory Physiology and Biophysics, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Ghazaleh Behnammanesh
- Department of Pharmacology and Therapeutics, University of Florida, Gainesville, FL, 32610, USA
| | - Jonathan E Bird
- Department of Pharmacology and Therapeutics, University of Florida, Gainesville, FL, 32610, USA
| | - Angela Ballesteros
- Section on Sensory Physiology and Biophysics, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, 20814, USA
| | | | - Albert S B Edge
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
| | - Michael R Deans
- Department of Neurobiology, Spencer Fox Eccles School of Medicine at the University of Utah, Salt Lake City, UT, 84112, USA
- Department of Otolaryngology - Head & Neck Surgery, Spencer Fox Eccles School of Medicine at the University of Utah, Salt Lake City, UT, 84132, USA
| | - Ksenia Gnedeva
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, 90033, USA
- Tina and Rick Caruso Department of Otolaryngology-Head and Neck Surgery, University of Southern California, Los Angeles, CA, 90033, USA
| | - Brikha R Shrestha
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
| | - Uri Manor
- Department of Cell and Developmental Biology, University of California San Diego, La Jolla, CA, 92093, USA
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Bo Zhao
- Department of Otolaryngology-Head and Neck Surgery, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Anthony J Ricci
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Basile Tarchini
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
- Tufts University School of Medicine, Boston, 02111, MA, USA
- Graduate School of Biomedical Science and Engineering (GSBSE), University of Maine, Orono, ME, 04469, USA
| | - Martín L Basch
- Department of Otolaryngology-Head and Neck Surgery, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Ruben Stepanyan
- Department of Otolaryngology-Head and Neck Surgery, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Department of Neurosciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Lukas D Landegger
- Department of Otolaryngology, Head and Neck Surgery, Vienna General Hospital and Medical University of Vienna, 1090, Vienna, Austria
- Department of Otolaryngology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Mark A Rutherford
- Department of Otolaryngology, Washington University, 660 S. Euclid Avenue, Campus Box 8115, St. Louis, MO, 63110, USA
| | - M Charles Liberman
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
- Speech and Hearing Biosciences and Technology graduate program, Harvard University, Cambridge, MA, 02138, USA
| | - Bradley J Walters
- The University of Mississippi Medical Center, Department of Otolaryngology - Head and Neck Surgery, Jackson, MS, 39216, USA
| | - Corné J Kros
- Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, UK
| | - Guy P Richardson
- Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, UK
| | - Lisa L Cunningham
- Section on Sensory Cell Biology, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Artur A Indzhykulian
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA.
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA.
- Speech and Hearing Biosciences and Technology graduate program, Harvard University, Cambridge, MA, 02138, USA.
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Stansak KL, Baum LD, Ghosh S, Thapa P, Vanga V, Walters BJ. PCP Auto Count: A Novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting. bioRxiv 2024:2024.01.30.578047. [PMID: 38352473 PMCID: PMC10862842 DOI: 10.1101/2024.01.30.578047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Background During development, planes of cells give rise to complex tissues and organs. The proper functioning of these tissues is critically dependent on proper inter- and intra-cellular spatial orientation, a feature known as planar cell polarity (PCP). To study the genetic and environmental factors affecting planar cell polarity investigators must often manually measure cell orientations, which is a time-consuming endeavor. Methodology To automate cell counting and planar cell polarity data collection we developed a Fiji/ImageJ plug-in called PCP Auto Count (PCPA). PCPA analyzes binary images and identifies "chunks" of white pixels that contain "caves" of infiltrated black pixels. Inner ear sensory epithelia including cochleae (P4) and utricles (E17.5) from mice were immunostained for βII-spectrin and imaged on a confocal microscope. Images were preprocessed using existing Fiji functionality to enhance contrast, make binary, and reduce noise. An investigator rated PCPA cochlear angle measurements for accuracy using a 1-5 agreement scale. For utricle samples, we directly compared PCPA derived measurements against manually derived angle measurements using concordance correlation coefficients (CCC) and Bland-Altman limits of agreement. Finally, PCPA was tested against a variety of images copied from publications examining PCP in various tissues and across various species. Results PCPA was able to recognize and count 99.81% of cochlear hair cells (n = 1,1541 hair cells) in a sample set, and was able to obtain ideally accurate planar cell polarity measurements for over 96% of hair cells. When allowing for a <10° deviation from "perfect" measurements, PCPA's accuracy increased to >98%. When manual angle measurements for E17.5 utricles were compared, PCPA's measurements fell within -9 to +10 degrees of manually obtained mean angle measures with a CCC of 0.999. Qualitative examination of example images of Drosophila ommatidia, mouse ependymal cells, and mouse radial progenitors revealed a high level of accuracy for PCPA across a variety of stains, tissue types, and species. Altogether, the data suggest that the PCPA plug-in suite is a robust and accurate tool for the automated collection of cell counts and PCP angle measurements.
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Affiliation(s)
- Kendra L. Stansak
- Department of Otolaryngology - Head and Neck Surgery, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Luke D. Baum
- Department of Otolaryngology - Head and Neck Surgery, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Sumana Ghosh
- Department of Otolaryngology - Head and Neck Surgery, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Punam Thapa
- Department of Otolaryngology - Head and Neck Surgery, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Vineel Vanga
- Department of Otolaryngology - Head and Neck Surgery, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Bradley J. Walters
- Department of Otolaryngology - Head and Neck Surgery, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
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Waldmann D, Lu Y, Cortada M, Bodmer D, Levano Huaman S. Exogenous humanin and MOTS-c function as protective agents against gentamicin-induced hair cell damage. Biochem Biophys Res Commun 2023; 678:115-121. [PMID: 37633181 DOI: 10.1016/j.bbrc.2023.08.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 08/16/2023] [Indexed: 08/28/2023]
Abstract
Loss of hair cells can lead to irreversible sensorineural hearing loss. Therefore, hair cell preservation is critical for hearing. Mitochondrial derived peptides (MDPs) are bioactive peptides and prominent members of this family are humanin (HN) and the mitochondrial-open-reading frame of the twelve S c (MOTS-c). The protective roles of HN and MOTS-c in age-related diseases and in various tissues exposed to cellular stresses have been demonstrated. The involvement of MDPs in the inner ear remains to be investigated. Therefore, we determined the expression of rattin, the homolog of humanin, in inner ear tissues. Then, we found that HN and MOTS-c showed a significant protective effect on hair cells in organ of Corti explants exposed to gentamicin. Treatment with HN decreased gentamicin-induced phosphorylation of AKT, whereas treatment with MOTS-c increased phosphorylation of AMPKα in explants. Our data indicate that MDPs exert a protective function in gentamicin-induced hair cell damage. Therefore, MDPs may contribute to design new preventive strategies against hearing loss.
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Affiliation(s)
- Dominique Waldmann
- University of Basel Hospital, Department of Biomedicine, Basel, Switzerland.
| | - Yu Lu
- University of Basel Hospital, Department of Biomedicine, Basel, Switzerland.
| | - Maurizio Cortada
- University of Basel Hospital, Department of Biomedicine, Basel, Switzerland; University of Basel Hospital, Clinic for Otolaryngology, Head and Neck Surgery, Basel, Switzerland.
| | - Daniel Bodmer
- University of Basel Hospital, Department of Biomedicine, Basel, Switzerland; University of Basel Hospital, Clinic for Otolaryngology, Head and Neck Surgery, Basel, Switzerland.
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4
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Buswinka CJ, Rosenberg DB, Simikyan RG, Osgood RT, Fernandez K, Nitta H, Hayashi Y, Liberman LW, Nguyen E, Yildiz E, Kim J, Jarysta A, Renauld J, Wesson E, Thapa P, Bordiga P, McMurtry N, Llamas J, Kitcher SR, López-Porras AI, Cui R, Behnammanesh G, Bird JE, Ballesteros A, Vélez-Ortega AC, Edge AS, Deans MR, Gnedeva K, Shrestha BR, Manor U, Zhao B, Ricci AJ, Tarchini B, Basch M, Stepanyan RS, Landegger LD, Rutherford M, Liberman MC, Walters BJ, Kros CJ, Richardson GP, Cunningham LL, Indzhykulian AA. Large-scale annotated dataset for cochlear hair cell detection and classification. bioRxiv 2023:2023.08.30.553559. [PMID: 37693382 PMCID: PMC10491224 DOI: 10.1101/2023.08.30.553559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Our sense of hearing is mediated by cochlear hair cells, localized within the sensory epithelium called the organ of Corti. There are two types of hair cells in the cochlea, which are organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains a few thousands of hair cells, and their survival is essential for our perception of sound because they are terminally differentiated and do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. However, the sheer number of cells along the cochlea makes manual quantification impractical. Machine learning can be used to overcome this challenge by automating the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, human, pig and guinea pig cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 90'000 hair cells, all of which have been manually identified and annotated as one of two cell types: inner hair cells and outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to supply other groups within the hearing research community with the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.
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Affiliation(s)
- Christopher J Buswinka
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
- Speech and Hearing Biosciences and Technology graduate program, Harvard University, Cambridge, MA, 02138, USA
| | - David B Rosenberg
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
| | - Rubina G Simikyan
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
| | - Richard T Osgood
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
- Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Katharine Fernandez
- Section on Sensory Cell Biology, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Hidetomi Nitta
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
| | - Yushi Hayashi
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
| | - Leslie W Liberman
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
| | - Emily Nguyen
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
| | - Erdem Yildiz
- Department of Otolaryngology, Head and Neck Surgery, Vienna General Hospital and Medical University of Vienna, 1090 Vienna, Austria
| | - Jinkyung Kim
- Department of Otolaryngology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Otolaryngology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - Justine Renauld
- Department of Otolaryngology-Head and Neck Surgery, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Ella Wesson
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
| | - Punam Thapa
- The University of Mississippi Medical Center, Dept. of Otolaryngology - Head and Neck Surgery, Jackson, MS, USA
| | - Pierrick Bordiga
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
| | - Noah McMurtry
- Department of Otolaryngology-Head and Neck Surgery, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Juan Llamas
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, 90033, USA
- Tina and Rick Caruso Department of Otolaryngology-Head and Neck Surgery, University of Southern California, Los Angeles, CA, 90033, USA
| | - Siân R Kitcher
- Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Ana I López-Porras
- Department of Physiology, University of Kentucky, Lexington, KY, 40536, USA
| | - Runjia Cui
- Section on Sensory Physiology and Biophysics, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Ghazaleh Behnammanesh
- Department of Pharmacology and Therapeutics, University of Florida, Gainesville, FL, 32610, USA; Myology Institute, University of Florida, Gainesville, FL, 32610, USA
| | - Jonathan E Bird
- Department of Pharmacology and Therapeutics, University of Florida, Gainesville, FL, 32610, USA; Myology Institute, University of Florida, Gainesville, FL, 32610, USA
| | - Angela Ballesteros
- Section on Sensory Physiology and Biophysics, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, 20814, USA
| | | | - Albert Sb Edge
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
| | - Michael R Deans
- Department of Neurobiology, Spencer Fox Eccles School of Medicine at the University of Utah, Salt Lake City, UT 84112, USA
- Department of Otolaryngology - Head & Neck Surgery, Spencer Fox Eccles School of Medicine at the University of Utah, Salt Lake City, UT, 84132, USA
| | - Ksenia Gnedeva
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, 90033, USA
- Tina and Rick Caruso Department of Otolaryngology-Head and Neck Surgery, University of Southern California, Los Angeles, CA, 90033, USA
| | - Brikha R Shrestha
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
| | - Uri Manor
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
- Department of Cell and Developmental Biology, University of California San Diego, La Jolla, CA, 92093
| | - Bo Zhao
- Department of Otolaryngology-Head and Neck Surgery, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Anthony J Ricci
- Department of Otolaryngology, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Basile Tarchini
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
- Department of Medicine, Tufts University, Boston, 02111, MA, USA
- Graduate School of Biomedical Science and Engineering (GSBSE), University of Maine, Orono, ME, 04469, USA
| | - Martin Basch
- Department of Otolaryngology-Head and Neck Surgery, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Ruben S Stepanyan
- Department of Otolaryngology-Head and Neck Surgery, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Department of Neurosciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Lukas D Landegger
- Department of Otolaryngology, Head and Neck Surgery, Vienna General Hospital and Medical University of Vienna, 1090 Vienna, Austria
| | - Mark Rutherford
- Department of Otolaryngology, Washington University, 660 S. Euclid Avenue, Campus Box 8115, St. Louis, MO, 63110, USA
| | - M Charles Liberman
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
- Speech and Hearing Biosciences and Technology graduate program, Harvard University, Cambridge, MA, 02138, USA
| | - Bradley J Walters
- The University of Mississippi Medical Center, Dept. of Otolaryngology - Head and Neck Surgery, Jackson, MS, USA
| | - Corné J Kros
- Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Guy P Richardson
- Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Lisa L Cunningham
- Section on Sensory Cell Biology, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Artur A Indzhykulian
- Eaton Peabody Laboratories, Mass Eye and Ear, Boston, MA, 02114, USA
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, 02114, USA
- Speech and Hearing Biosciences and Technology graduate program, Harvard University, Cambridge, MA, 02138, USA
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5
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Ma T, Wu Q, Jiang L, Zeng X, Wang Y, Yuan Y, Wang B, Zhang T. Artificial Intelligence and Machine (Deep) Learning in Otorhinolaryngology: A Bibliometric Analysis Based on VOSviewer and CiteSpace. Ear Nose Throat J 2023:1455613231185074. [PMID: 37515527 DOI: 10.1177/01455613231185074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/31/2023] Open
Abstract
BACKGROUND Otorhinolaryngology diseases are well suited for artificial intelligence (AI)-based interpretation. The use of AI, particularly AI based on deep learning (DL), in the treatment of human diseases is becoming more and more popular. However, there are few bibliometric analyses that have systematically studied this field. OBJECTIVE The objective of this study was to visualize the research hot spots and trends of AI and DL in ENT diseases through bibliometric analysis to help researchers understand the future development of basic and clinical research. METHODS In all, 232 articles and reviews were retrieved from The Web of Science Core Collection. Using CiteSpace and VOSviewer software, countries, institutions, authors, references, and keywords in the field were visualized and examined. RESULTS The majority of these papers came from 44 nations and 498 institutions, with China and the United States leading the way. Common diseases used by AI in ENT include otosclerosis, otitis media, nasal polyps, sinusitis, and so on. In the early years, research focused on the analysis of hearing and articulation disorders, and in recent years mainly on the diagnosis, localization, and grading of diseases. CONCLUSIONS The analysis shows the periodical hot spots and development direction of AI and DL application in ENT diseases from the time dimension. The diagnosis and prognosis of otolaryngology diseases and the analysis of otolaryngology endoscopic images have been the focus of current research and the development trend of future.
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Affiliation(s)
- Tianyu Ma
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qilong Wu
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Li Jiang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiaoyun Zeng
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yuyao Wang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yi Yuan
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Bingxuan Wang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianhong Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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Buswinka CJ, Osgood RT, Simikyan RG, Rosenberg DB, Indzhykulian AA. The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification. PLoS Biol 2023; 21:e3002041. [PMID: 36947567 PMCID: PMC10069775 DOI: 10.1371/journal.pbio.3002041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 04/03/2023] [Accepted: 02/17/2023] [Indexed: 03/23/2023] Open
Abstract
Our sense of hearing is mediated by sensory hair cells, precisely arranged and highly specialized cells subdivided into outer hair cells (OHCs) and inner hair cells (IHCs). Light microscopy tools allow for imaging of auditory hair cells along the full length of the cochlea, often yielding more data than feasible to manually analyze. Currently, there are no widely applicable tools for fast, unsupervised, unbiased, and comprehensive image analysis of auditory hair cells that work well either with imaging datasets containing an entire cochlea or smaller sampled regions. Here, we present a highly accurate machine learning-based hair cell analysis toolbox (HCAT) for the comprehensive analysis of whole cochleae (or smaller regions of interest) across light microscopy imaging modalities and species. The HCAT is a software that automates common image analysis tasks such as counting hair cells, classifying them by subtype (IHCs versus OHCs), determining their best frequency based on their location along the cochlea, and generating cochleograms. These automated tools remove a considerable barrier in cochlear image analysis, allowing for faster, unbiased, and more comprehensive data analysis practices. Furthermore, HCAT can serve as a template for deep learning-based detection tasks in other types of biological tissue: With some training data, HCAT's core codebase can be trained to develop a custom deep learning detection model for any object on an image.
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Affiliation(s)
- Christopher J Buswinka
- Mass Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States of America
- Speech and Hearing Bioscience and Technology Program, Harvard University, Cambridge, Massachusetts, United States of America
| | - Richard T Osgood
- Mass Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Rubina G Simikyan
- Mass Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States of America
| | - David B Rosenberg
- Mass Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Artur A Indzhykulian
- Mass Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States of America
- Speech and Hearing Bioscience and Technology Program, Harvard University, Cambridge, Massachusetts, United States of America
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Kurnia KA, Sampurna BP, Audira G, Juniardi S, Vasquez RD, Roldan MJM, Tsao C, Hsiao C. Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension. Int J Mol Sci 2022; 23:6009. [PMID: 35682689 PMCID: PMC9181243 DOI: 10.3390/ijms23116009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 12/04/2022] Open
Abstract
Previous methods to measure protozoan numbers mostly rely on manual counting, which suffers from high variation and poor efficiency. Although advanced counting devices are available, the specialized and usually expensive machinery precludes their prevalent utilization in the regular laboratory routine. In this study, we established the ImageJ-based workflow to quantify ciliate numbers in a high-throughput manner. We conducted Tetrahymena number measurement using five different methods: particle analyzer method (PAM), find maxima method (FMM), trainable WEKA segmentation method (TWS), watershed segmentation method (WSM) and StarDist method (SDM), and compared their results with the data obtained from the manual counting. Among the five methods tested, all of them could yield decent results, but the deep-learning-based SDM displayed the best performance for Tetrahymena cell counting. The optimized methods reported in this paper provide scientists with a convenient tool to perform cell counting for Tetrahymena ecotoxicity assessment.
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