OBJECTIVE: We describe and evaluate an automated software tool for nerve-fiber detection and quantification in corneal confocal microscopy (CCM) images, combining sensitive nerve- fiber detection with morphological descriptors. METHOD: We have evaluated the tool for quantification of Diabetic Sensorimotor Polyneuropathy (DSPN) using both new and previously published morphological features. The evaluation used 888 images from 176 subjects (84 controls and 92 patients with type 1 diabetes). The patient group was further subdivided into those with ( n = 63) and without ( n = 29) DSPN. RESULTS: We achieve improved nerve- fiber detection over previous results (91.7% sensitivity and specificity in identifying nerve-fiber pixels). Automatic quantification of nerve morphology shows a high correlation with previously reported, manually measured, features. Receiver Operating Characteristic (ROC) analysis of both manual and automatic measurement regimes resulted in similar results in distinguishing patients with DSPN from those without: AUC of about 0.77 and 72% sensitivity-specificity at the equal error rate point. CONCLUSION: Automated quantification of corneal nerves in CCM images provides a sensitive tool for identification of DSPN. Its performance is equivalent to manual quantification, while improving speed and repeatability. SIGNIFICANCE: CCM is a novel in vivo imaging modality that has the potential to be a noninvasive and objective image biomarker for peripheral neuropathy. Automatic quantification of nerve morphology is a major step forward in the early diagnosis and assessment of progression, and, in particular, for use in clinical trials to establish therapeutic benefit in diabetic and other peripheral neuropathies.
OBJECTIVE: We describe and evaluate an automated software tool for nerve-fiber detection and quantification in corneal confocal microscopy (CCM) images, combining sensitive nerve- fiber detection with morphological descriptors. METHOD: We have evaluated the tool for quantification of Diabetic Sensorimotor Polyneuropathy (DSPN) using both new and previously published morphological features. The evaluation used 888 images from 176 subjects (84 controls and 92 patients with type 1 diabetes). The patient group was further subdivided into those with ( n = 63) and without ( n = 29) DSPN. RESULTS: We achieve improved nerve- fiber detection over previous results (91.7% sensitivity and specificity in identifying nerve-fiber pixels). Automatic quantification of nerve morphology shows a high correlation with previously reported, manually measured, features. Receiver Operating Characteristic (ROC) analysis of both manual and automatic measurement regimes resulted in similar results in distinguishing patients with DSPN from those without: AUC of about 0.77 and 72% sensitivity-specificity at the equal error rate point. CONCLUSION: Automated quantification of corneal nerves in CCM images provides a sensitive tool for identification of DSPN. Its performance is equivalent to manual quantification, while improving speed and repeatability. SIGNIFICANCE: CCM is a novel in vivo imaging modality that has the potential to be a noninvasive and objective image biomarker for peripheral neuropathy. Automatic quantification of nerve morphology is a major step forward in the early diagnosis and assessment of progression, and, in particular, for use in clinical trials to establish therapeutic benefit in diabetic and other peripheral neuropathies.
Authors: Mitra Tavakoli; Andy Marshall; Siddharth Banka; Ioannis N Petropoulos; Hassan Fadavi; Helen Kingston; Rayaz A Malik Journal: Muscle Nerve Date: 2012-09-19 Impact factor: 3.217
Authors: Michael Brines; Ann N Dunne; Monique van Velzen; Paolo L Proto; Claes-Goran Ostenson; Rita I Kirk; Ioannis N Petropoulos; Saad Javed; Rayaz A Malik; Anthony Cerami; Albert Dahan Journal: Mol Med Date: 2015-03-13 Impact factor: 6.354
Authors: Peter J Dyck; Carol J Overland; Phillip A Low; William J Litchy; Jenny L Davies; P James B Dyck; Peter C O'Brien; James W Albers; Henning Andersen; Charles F Bolton; John D England; Christopher J Klein; J Gareth Llewelyn; Michelle L Mauermann; James W Russell; Wolfgang Singer; A Gordon Smith; Solomon Tesfaye; Adrian Vella Journal: Muscle Nerve Date: 2010-08 Impact factor: 3.217
Authors: R A Malik; P Kallinikos; C A Abbott; C H M van Schie; P Morgan; N Efron; A J M Boulton Journal: Diabetologia Date: 2003-05-09 Impact factor: 10.122
Authors: Mitra Tavakoli; Cristian Quattrini; Caroline Abbott; Panagiotis Kallinikos; Andrew Marshall; Joanne Finnigan; Philip Morgan; Nathan Efron; Andrew J M Boulton; Rayaz A Malik Journal: Diabetes Care Date: 2010-04-30 Impact factor: 19.112
Authors: Aleksandra Matuszewska-Iwanicka; Bernd Stratmann; Oliver Stachs; Stephan Allgeier; Andreas Bartschat; Karsten Winter; Rudolf Guthoff; Diethelm Tschoepe; Hans-Joachim Hettlich Journal: Ophthalmol Ther Date: 2022-10-03
Authors: Md Asif Khan Setu; Stefan Schmidt; Gwen Musial; Michael E Stern; Philipp Steven Journal: Transl Vis Sci Technol Date: 2022-06-01 Impact factor: 3.048
Authors: Megan E McCarron; Rachel L Weinberg; Jessica M Izzi; Suzanne E Queen; Patrick M Tarwater; Stuti L Misra; Daniel B Russakoff; Jonathan D Oakley; Joseph L Mankowski Journal: Cornea Date: 2021-05-01 Impact factor: 3.152
Authors: Xin Chen; Jim Graham; Ioannis N Petropoulos; Georgios Ponirakis; Omar Asghar; Uazman Alam; Andrew Marshall; Maryam Ferdousi; Shazli Azmi; Nathan Efron; Rayaz A Malik Journal: Invest Ophthalmol Vis Sci Date: 2018-02-01 Impact factor: 4.799
Authors: Bruce A Perkins; Leif Erik Lovblom; Evan J H Lewis; Vera Bril; Maryam Ferdousi; Andrej Orszag; Katie Edwards; Nicola Pritchard; Anthony Russell; Cirous Dehghani; Danièle Pacaud; Kenneth Romanchuk; Jean K Mah; Maria Jeziorska; Andrew Marshall; Roni M Shtein; Rodica Pop-Busui; Stephen I Lentz; Mitra Tavakoli; Andrew J M Boulton; Nathan Efron; Rayaz A Malik Journal: Diabetes Care Date: 2021-07-01 Impact factor: 17.152