PMID- 35686708
OWN - NLM
STAT- MEDLINE
DCOM- 20220613
LR  - 20220613
IS  - 1938-2391 (Electronic)
IS  - 1081-597X (Linking)
VI  - 38
IP  - 6
DP  - 2022 Jun
TI  - Combining Spectral-Domain OCT and Air-Puff Tonometry Analysis to Diagnose 
      Keratoconus.
PG  - 374-380
LID - 10.3928/1081597X-20220414-02 [doi]
AB  - PURPOSE: To investigate the diagnostic capacity of spectral-domain optical 
      coherence tomography (SD-OCT) combined with air-puff tonometry using artificial 
      intelligence (AI) in differentiating between normal and keratoconic eyes. 
      METHODS: Patients who had either undergone uneventful laser vision correction 
      with at least 3 years of stable follow-up or those who had forme fruste 
      keratoconus (FFKC), early keratoconus (EKC), or advanced keratoconus (AKC) were 
      included. SD-OCT and biomechanical information from air-puff tonometry was 
      divided into training and validation sets. AI models based on random forest or 
      neural networks were trained to distinguish eyes with FFKC from normal eyes. 
      Model accuracy was independently tested in eyes with FFKC and normal eyes. 
      Receiver operating characteristic (ROC) curves were generated to determine area 
      under the curve (AUC), sensitivity, and specificity values. RESULTS: A total of 
      223 normal eyes from 223 patients, 69 FFKC eyes from 69 patients, 72 EKC eyes 
      from 72 patients, and 258 AKC eyes from 258 patients were included. The top AUC 
      ROC values (normal eyes compared with AKC and EKC) were Pentacam Random Forest 
      Index (AUC = 0.985 and 0.958), Tomographic and Biomechanical Index (AUC = 0.983 
      and 0.925), and Belin-Ambrosio Enhanced Ectasia Total Deviation Index (AUC = 
      0.981 and 0.922). When SD-OCT and air-puff tonometry data were combined, the 
      random forest AI model provided the highest accuracy with 99% AUC for FFKC (75% 
      sensitivity; 94.74% specificity). CONCLUSIONS: Currently, AI parameters 
      accurately diagnose AKC and EKC, but have a limited ability to diagnose FFKC. 
      AI-assisted diagnostic technology that uses both SD-OCT and air-puff tonometry 
      may overcome this limitation, leading to improved treatment of patients with 
      keratoconus. [J Refract Surg. 2022;38(6):374-380.].
FAU - Lu, Nan-Ji
AU  - Lu NJ
FAU - Elsheikh, Ahmed
AU  - Elsheikh A
FAU - Rozema, Jos J
AU  - Rozema JJ
FAU - Hafezi, Nikki
AU  - Hafezi N
FAU - Aslanides, Ioannis M
AU  - Aslanides IM
FAU - Hillen, Mark
AU  - Hillen M
FAU - Eckert, Daniel
AU  - Eckert D
FAU - Funck, Christian
AU  - Funck C
FAU - Koppen, Carina
AU  - Koppen C
FAU - Cui, Le-Le
AU  - Cui LL
FAU - Hafezi, Farhad
AU  - Hafezi F
LA  - eng
PT  - Journal Article
DEP - 20220601
PL  - United States
TA  - J Refract Surg
JT  - Journal of refractive surgery (Thorofare, N.J. : 1995)
JID - 9505927
SB  - IM
MH  - Artificial Intelligence
MH  - Cornea
MH  - Corneal Pachymetry
MH  - Corneal Topography/methods
MH  - Humans
MH  - *Keratoconus/diagnosis
MH  - Manometry
MH  - ROC Curve
MH  - Retrospective Studies
MH  - Tomography, Optical Coherence
EDAT- 2022/06/11 06:00
MHDA- 2022/06/14 06:00
CRDT- 2022/06/10 07:33
PHST- 2022/06/10 07:33 [entrez]
PHST- 2022/06/11 06:00 [pubmed]
PHST- 2022/06/14 06:00 [medline]
AID - 10.3928/1081597X-20220414-02 [doi]
PST - ppublish
SO  - J Refract Surg. 2022 Jun;38(6):374-380. doi: 10.3928/1081597X-20220414-02. Epub 
      2022 Jun 1.