Diagnostic value of telecytology in tertiary teledermatological consultation: a retrospective analysis of 75 cases

Durdu, Murat; Harman, Mehmet

doi:10.1111/ijd.13219

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Nevertheless, in most clinics, this simple test is either not performed or is only used to diagnose a few diseases, mainly due to lack of experience. In a dermatology clinic where 75 patients for whom no diagnosis could be established via face-to-face clinical examinations and cytological evaluations, telemedicine was used to overcome this issue 34 . Although this is a reasonable solution, the necessary expertise is still scarce, scheduling is necessary, and one can only examine a limited number of images during a session.…”

Section: Discussionmentioning

confidence: 99%

TzanckNet: a convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases

Noyan¹,

Durdu

Eskiocak

2020

Sci Rep

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Tzanck smear test is a low-cost, rapid and reliable tool which can be used for the diagnosis of many erosive-vesiculobullous, tumoral and granulomatous diseases. Currently its use is limited mainly due to lack of experience in interpretation of the smears. We developed a deep learning model, TzanckNet, that can identify cells in Tzanck smear test findings. TzanckNet was trained on a retrospective development dataset of 2260 Tzanck smear images collected between December 2006 and December 2019. The finalized model was evaluated using a prospective validation dataset of 359 Tzanck smear images collected from 15 patients during January 2020. It is designed to recognize six cell types (acantholytic cells, eosinophils, hypha, multinucleated giant cells, normal keratinocytes and tadpole cells). For 359 images and 6 cell types, TzanckNet made 2154 predictions. The accuracy was 94.3% (95% CI 93.4–95.3), the sensitivity was 83.7% (95% CI 80.3–87.0) and the specificity was 97.3% (95% CI 96.5–98.1). The area under the receiver operating characteristic curve was 0.974. Our results show that TzanckNet has the potential to lower the experience barrier needed to use this test, broadening its user base, and hence improving patient well-being.

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Section: Discussionmentioning

confidence: 99%

TzanckNet: a convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases

Noyan¹,

Durdu

Eskiocak

2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a dermatology clinic where 75 patients for whom no diagnosis could be established via face-to-face clinical examinations and cytological evaluations, telemedicine was used to overcome this issue. 34 Although this is a reasonable solution, the necessary expertise is still scarce, scheduling is necessary, and one can only examine a limited number of images during a session. In this work we demonstrated that TzanckNet can identify six cell types in Tzanck smear findings.…”

Section: Discussionmentioning

confidence: 99%

TzanckNet: A convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases

Noyan¹,

Durdu

Eskiocak

2020

Preprint

Self Cite

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Tzanck smear test is a low-cost, rapid, and reliable tool which can be used for the diagnosis of many erosive-vesiculobullous, tumoral, and granulomatous diseases. Currently its use is limited mainly due to lack of experience in interpretation of the smears. We developed a deep learning model, TzanckNet, that can identify cells in Tzanck smear test findings. TzanckNet was trained on a retrospective development dataset of 2260 Tzanck smear images collected between December 2006 - December 2019. The finalized model was evaluated using a prospective validation dataset of 359 Tzanck smear images collected from 15 patients during January 2020. It is designed to recognize six cell types (acantholytic cells, eosinophils, hypha, multinucleated giant cells, normal keratinocytes, and tadpole cells). For 359 images and 6 cell types, TzanckNet made 2154 predictions. The accuracy was 94.3 % (95% CI 93.4 to 95.3), the sensitivity was 83.7 % (95% CI 80.3 to 87.0) and the specificity was 97.3 % (95% CI 96.5 to 98.1). The area under the receiver operating characteristic curve was 0.974. Our results show that TzanckNet has the potential to lower the experience barrier needed to use this test, broadening its user base, and hence improving patient well-being.

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A Systematic Review of the Use of Telemedicine in Plastic and Reconstructive Surgery and Dermatology

Vyas¹,

Hambrick²,

Shakir³

et al. 2017

Annals of Plastic Surgery

119

101

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Telemedicine holds special promise in increasing the efficiency of postoperative care for microsurgical procedures, improving care coordination and management of burn wounds, facilitating interprofessional collaboration across time and space, eliminating a significant number of unnecessary referrals, and connecting patients located far from major medical centers with professional expertise without impinging on-and in some cases improving-the quality or accuracy of care provided. Teledermatology consultation was found to be safe and has a comparable or superior efficacy to the traditional in-patient consultation. The system was consistently rated as convenient and easy to use by patients, referring physicians, and consulting dermatologists. Teledermatology has also been used as an educational tool for patients. A significant number of studies detailed strategies to improve the current state of teledermatology, either by implementing new programs or improving technologies. Telemedicine use is widespread among plastic surgeons and is enabling the spread of expertise beyond major medical centers. Further research is needed to conclusively demonstrate benefit in routine clinical care.

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Diagnostic value of telecytology in tertiary teledermatological consultation: a retrospective analysis of 75 cases

Abstract: This study revealed that the cytological images should be used in tertiary teledermatological evaluation. Further studies should therefore be carried out to investigate the diagnostic value of different telecytological methods.

Cited by 5 publications

References 15 publications

TzanckNet: a convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases

TzanckNet: a convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases

TzanckNet: A convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases

A Systematic Review of the Use of Telemedicine in Plastic and Reconstructive Surgery and Dermatology

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