Cited by 159Cutaneous Melanoma Detection and ManagementAI in cancer detectionmelanin and skin pigmentation

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Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study

Piccolo, D., Ferrari, A., Peris, K., Daidone, R., Ruggeri, B., Chimenti, S.·2002·British Journal of Dermatology

DOI 10.1046/j.1365-2133.2002.04978.x

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Abstract

BACKGROUND: In the last few years digital dermoscopy has been introduced as an additional tool to improve the clinical diagnosis of pigmented skin lesions. OBJECTIVE: To evaluate the validity of digital dermoscopy by comparing the diagnoses of a dermatologist experienced in dermoscopy (5 years of experience) with those of a clinician with minimal training in this field, and then comparing these results with those obtained using computer-aided diagnoses. METHODS: Three hundred and forty-one pigmented melanocytic and non-melanocytic skin lesions were included. All lesions were surgically excised and histopathologically examined. Digital dermoscopic images of all lesions were framed and analysed using software based on a trained artificial neural network. Cohen's kappa statistic was calculated to assess the validity with regard to the correct diagnoses of melanoma and non-melanoma. RESULTS: Sensitivity was high for the experienced dermatologist and the computer (92%) and lower for the inexperienced clinician (69%). Specificity of the diagnosis by the experienced dermatologist was higher (99%) than that of the inexperienced clinician (94%) and the computer assessment (74%). Notably, computer analysis gave a higher number of false positives (26%) compared with the experienced dermatologist (0.6%) and the inexperienced clinician (5.5%). CONCLUSIONS: Our results indicate that analysis either by a trained dermatologist or an artificial neural network-trained computer can improve the diagnostic accuracy of melanoma compared with that of an inexperienced clinician and that the computer diagnosis might represent a useful tool for the screening of melanoma, particularly at centres not experienced in dermoscopy.

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Statistical Methods in Medical Research.
Armitage, P. (1972) · Biometrics · doi:10.2307/2556182 · Cited by 5,383
Epiluminescence Microscopy for the Diagnosis of Doubtful Melanocytic Skin Lesions
Argenziano, G., Fabbrocini, G., Carli, P., GIORGI, V. D., Sammarco, E., & Delfino, M. (1998) · Archives of Dermatology · doi:10.1001/archderm.134.12.1563 · Cited by 898
The ABCD rule of dermatoscopy
Nachbar, F., Stolz, W., Merkle, T., Cognetta, A. B., VOGT, T. M., Landthaler, M., Bilek, P., Braun‐Falco, O., & Plewig, G. (1994) · Journal of the American Academy of Dermatology · doi:10.1016/s0190-9622(94)70061-3 · Cited by 893
In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions
Pehamberger, H., Steiner, A., & Wolff, K. (1987) · Journal of the American Academy of Dermatology · doi:10.1016/s0190-9622(87)70239-4 · Cited by 634
Epiluminescence Microscopy
Binder, M. (1995) · Archives of Dermatology · doi:10.1001/archderm.131.3.286 · Cited by 539
Dermoscopy of pigmented skin lesions – a valuable tool for early
Argenziano, G., & Soyer, H. P. (2001) · The Lancet Oncology · doi:10.1016/s1470-2045(00)00422-8 · Cited by 391
In Vivo Epiluminescence Microscopy: Improvement of Early Diagnosis of Melanoma
Pehamberger, H., Binder, M., Steiner, A., & Wolff, K. S. (1993) · Journal of Investigative Dermatology · doi:10.1038/jid.1993.63 · Cited by 343
A sensitivity and specificity analysis of the surface microscopy features of invasive melanoma
Menzies, S. W., Ingvar, C., & McCarthy, W. H. (1996) · Melanoma Research · doi:10.1097/00008390-199602000-00008 · Cited by 269
Clinical diagnosis of pigmented lesions using digital epiluminescence microscopy. Grading protocol and atlas
Kenet, R. O. (1993) · Archives of Dermatology · doi:10.1001/archderm.129.2.157 · Cited by 261
Terminology in surface microscopy. Consensus meeting of the Committee on Analytical Morphology of the Arbeitsgemeinschaft Dermatologische Forschung, Hamburg, Federal Republic of Germany, Nov. 17, 1989.
Bahmer, F. A., Fritsch, P. O., Kreusch, J. F., Pehamberger, H., Rohrer, C., Schindera, I., Smolle, J., Soyer, H. P., & Stolz, W. (1990) · PubMed · Cited by 155

Show all 26 references

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In Vivo Microscopic Approaches for Facial Melanocytic Lesions after Quality-Switched Ruby Laser Therapy: Time-Sequential Imaging of Melanin and Melanocytes of Solar Lentigo in Asian Skin
Yamashita, T., Negishi, K., Hariya, T., Yanai, M., Iikura, T., & Wakamatsu, S. (2010) · Dermatologic Surgery · doi:10.1111/j.1524-4725.2010.01598.x · Cited by 18
Advances and short comings in the early diagnosis of melanoma
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Sethy, P. K., Behera, S. K., & Kannan, N. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00632-9 · Cited by 17
Improved Skin Lesion Diagnostics for General Practice by Computer-Aided Diagnostics
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Classification of reticular pattern and streaks in dermoscopic images based on texture analysis
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Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method
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The value of (video)dermoscopy in the diagnosis and monitoring of common inflammatory skin diseases: a systematic review
Vos, M. H. E., Nguyen, K. P., van Erp, P. E. J., van de Kerkhof, P. C. M., Driessen, R. J. B., & Peppelman, M. (2018) · European Journal of Dermatology · doi:10.1684/ejd.2018.3396 · Cited by 15
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Systematic Review of Deep Learning Techniques in Skin Cancer Detection
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Tognetti, L., Miracapillo, C., Leonardelli, S., Luschi, A., Iadanza, E., Cevenini, G., Rubegni, P., & Cartocci, A. (2024) · Bioengineering · doi:10.3390/bioengineering11080758 · Cited by 13
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Mabrouk, M. S., Sheha, M. A., & Sharawy, A. A. (2013) · INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY · doi:10.24297/ijct.v10i8.1474 · Cited by 12
Ten reasons why dermoscopy is beneficial for the evaluation of skin lesions
Benvenuto-Andrade, C., & Marghoob, A. A. (2006) · Expert Review of Dermatology · doi:10.1586/17469872.1.3.369 · Cited by 12
Electrical impedance of human skin and tissue alterations : Mathematical modeling and measurements
Birgersson, U. (2012) · Figshare · Cited by 12
[Retracted] Antialiasing Attention Spatial Convolution Model for Skin Lesion Segmentation with Applications in the Medical IoT
Le, P. T., Chang, C., Li, Y., Hsu, Y., & Wang, J. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/1278515 · Cited by 11
Retracted
Where's the naevus? Inter‐operator variability in the localization of melanocytic lesion border
Fortina, A. B., Peserico, E., Silletti, A., & Zattra, E. (2011) · Skin Research and Technology · doi:10.1111/j.1600-0846.2011.00572.x · Cited by 11
On the geometric modulation of skin lesion growth: a mathematical model for melanoma
Mendes, A. I., Nogueira, C. V., Pereira, J., & Fonseca-Pinto, R. (2016) · Research on Biomedical Engineering · doi:10.1590/2446-4740.02815 · Cited by 11
Diagnostic Accuracy of Novel Optical Imaging Techniques for Melanoma Detection: A Systematic Review and Meta‐Analysis
Varga, N., Gulyás, L., Meznerics, F., Barkovskij-Jakobsen, K. S., Szabó, B., Hegyi, P., Bánvölgyi, A., Medvecz, M., & Kiss, N. (2025) · International Journal of Dermatology · doi:10.1111/ijd.17828 · Cited by 10
Near infrared and skin impedance spectroscopy – a possible support in the diagnostic process of skin tumours in primary health care
Hägerlind, E., Falk, M., Löfstedt, T., Lindholm‐Sethson, B., & Bodén, I. (2015) · Skin Research and Technology · doi:10.1111/srt.12219 · Cited by 10
Update and Clinical Use of Imaging Technologies for Pigmented Lesions of the Skin
O'Donnell, A. T., & Kim, C. C. (2012) · Seminars in Cutaneous Medicine and Surgery · doi:10.1016/j.sder.2011.12.003 · Cited by 10
Level Set Initialization Based on Modified Fuzzy C Means Thresholding for Automated Segmentation of Skin Lesions
Masood, A., Al-Jumaily, A. A., & Maali, Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-42051-1_43 · Cited by 10
Strategien zur nichtinvasiven Diagnostik des Melanoms
Fink, C., & Haenssle, H. A. (2016) · Der Hautarzt · doi:10.1007/s00105-016-3796-0 · Cited by 10

Authors: Piccolo, D., Ferrari, A., Peris, K., Daidone, R., Ruggeri, B., & Chimenti, S.
Affiliations: University of L'Aquila (IT)
University of Rome Tor Vergata (IT)
Corresponding author: Peris, K.
ORCID: Piccolo, D.
Ferrari, A.
Peris, K.
Ruggeri, B.
Chimenti, S.
Venue: British Journal of Dermatology
Publisher: Oxford University Press (OUP)
Published: 2002-09
Volume: 147
Issue: 3
Pages: 481-486
ISSN: 0007-0963 (ISSN-L)
1365-2133
DOI: 10.1046/j.1365-2133.2002.04978.x
OpenAlex: W2065829097
Cited by: 159
References: 26
Citations by year: 2026: 5 · 2025: 6 · 2024: 10 · 2023: 4 · 2022: 7 · 2021: 10 · 2020: 8 · 2019: 7 · 2018: 6 · 2017: 9 · 2016: 8 · 2015: 9 · 2014: 4 · 2013: 9 · 2012: 12
FWCI: 1.63
Type: journal article
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 4748 d)
Locations: British Journal of Dermatology (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › melanin and skin pigmentation
Keywords: digital dermoscopy, pigmented skin lesions, melanoma diagnosis, computer-aided diagnosis, artificial neural networks, dermoscopic diagnosis, diagnostic accuracy, melanoma screening, melanocytic lesions, false positives
MeSH terms: Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Clinical Competence, Dermatology, Diagnosis, Computer-Assisted, Diagnosis, Differential, Female, Humans, Image Processing, Computer-Assisted, Male, Melanoma, Middle Aged, Nevus, Pigmented, Predictive Value of Tests, Sensitivity and Specificity, Skin Neoplasms, Reproducibility of Results, Neural Networks, Computer, Microscopy, Video

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Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study

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