TY - JOUR

T1 - Explainable Image Quality Assessments in Teledermatological Photography

AU - Jalaboi, Raluca

AU - Winther, Ole

AU - Galimzianova, Alfiia

PY - 2023

Y1 - 2023

N2 - Background and Objectives: Image quality is a crucial factor in the effectiveness and efficiency of teledermatological consultations. However, up to 50% of images sent by patients have quality issues, thus increasing the time to diagnosis and treatment. An automated, easily deployable, explainable method for assessing image quality is necessary to improve the current teledermatological consultation flow. We introduce ImageQX, a convolutional neural network for image quality assessment with a learning mechanism for identifying the most common poor image quality explanations: bad framing, bad lighting, blur, low resolution, and distance issues.Methods: ImageQX was trained on 26,635 photographs and validated on 9,874 photographs, each annotated with image quality labels and poor image quality explanations by up to 12 board-certified dermatologists. The photographic images were taken between 2017 and 2019 using a mobile skin disease tracking application accessible worldwide.Results: Our method achieves expert-level performance for both image quality assessment and poor image quality explanation. For image quality assessment, ImageQX obtains a macro F1-score of 0.73 +/- 0.01, which places it within standard deviation of the pairwise inter-rater F1-score of 0.77 +/- 0.07. For poor image quality explanations, our method obtains F1-scores of between 0.37 +/- 0.01 and 0.70 +/- 0.01, similar to the inter-rater pairwise F1-score of between 0.24 +/- 0.15 and 0.83 +/- 0.06. Moreover, with a size of only 15 MB, ImageQX is easily deployable on mobile devices.Conclusion: With an image quality detection performance similar to that of dermatologists, incorporating ImageQX into the teledermatology flow can enable a better, faster flow for remote consultations.

AB - Background and Objectives: Image quality is a crucial factor in the effectiveness and efficiency of teledermatological consultations. However, up to 50% of images sent by patients have quality issues, thus increasing the time to diagnosis and treatment. An automated, easily deployable, explainable method for assessing image quality is necessary to improve the current teledermatological consultation flow. We introduce ImageQX, a convolutional neural network for image quality assessment with a learning mechanism for identifying the most common poor image quality explanations: bad framing, bad lighting, blur, low resolution, and distance issues.Methods: ImageQX was trained on 26,635 photographs and validated on 9,874 photographs, each annotated with image quality labels and poor image quality explanations by up to 12 board-certified dermatologists. The photographic images were taken between 2017 and 2019 using a mobile skin disease tracking application accessible worldwide.Results: Our method achieves expert-level performance for both image quality assessment and poor image quality explanation. For image quality assessment, ImageQX obtains a macro F1-score of 0.73 +/- 0.01, which places it within standard deviation of the pairwise inter-rater F1-score of 0.77 +/- 0.07. For poor image quality explanations, our method obtains F1-scores of between 0.37 +/- 0.01 and 0.70 +/- 0.01, similar to the inter-rater pairwise F1-score of between 0.24 +/- 0.15 and 0.83 +/- 0.06. Moreover, with a size of only 15 MB, ImageQX is easily deployable on mobile devices.Conclusion: With an image quality detection performance similar to that of dermatologists, incorporating ImageQX into the teledermatology flow can enable a better, faster flow for remote consultations.

KW - teledermatology

KW - image quality

KW - artificial intelligence

KW - deep learning

KW - explainability

KW - telemedicine

U2 - 10.1089/tmj.2022.0405

DO - 10.1089/tmj.2022.0405

M3 - Journal article

C2 - 36735575

VL - 29

SP - 1342

EP - 1348

JO - Telemedicine Journal and e-Health

JF - Telemedicine Journal and e-Health

SN - 1530-5627

IS - 9

ER -