31 research outputs found
Nevi in children (Part 1) epidermal nevi: Clinical picture, diagnosis, treatment
Nevi are congenital formations that appear on the skin from birth or in early childhood, are very common in healthy children and, as a rule, are harmless. The article deals with epidermal nevi formed from epidermal cells. Particular attention is paid to the syndromes of epidermal nevi, which are characterized by a combination of skin rashes with systemic manifestations. Correct diagnosis of different subtypes of nevi, their differential diagnosis with other pigment formations (including melanomas) and non-melanoma skin cancer, as well as the recognition of non-uniform syndromes will help to determine the pediatrician correct tactics of management of patients, further counseling and assess the prognosis of the disease. Early diagnosis using dermatoscopy and modern techniques based on artificial intelligence is most significant in children before the development of progressive symptoms or neurological disorders. In the detection of epidermal nevus syndromes, consultations of related specialists (neurologists, traumatologists, cardiologists, etc.) are recommended. Β© INRA and Springer-Verlag France 2015
ΠΠΎΠΏΡΠΎΡΡ Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Π½ΠΎΠ²ΠΎΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ ΠΊΠΎΠΆΠΈ Π² ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΡΡ ΠΏΡΠ°ΠΊΡΠΈΠΊΡ
Despite the existence of many algorithms for automated diagnosis of melanoma and other skin cancers, these remain almost inaccessible to public health service. A small number of publications on the efficacy of existing artificial intelligence systems marks the problems of their implementation into current examination routines in dermatology and oncology. New algorithms and software solutions as well as studies demonstrating their diagnostic accuracy on compatible and verifiable clinical material are still in demand.ΠΠ΅ΡΠΌΠΎΡΡΡ Π½Π° ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²ΠΎ ΠΈΠΌΠ΅ΡΡΠΈΡ
ΡΡ ΠΈ ΡΠ°Π·ΡΠ°Π±Π°ΡΡΠ²Π°Π΅ΠΌΡΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΌΠ΅Π»Π°Π½ΠΎΠΌΡ ΠΈ Π΄ΡΡΠ³ΠΈΡ
Π·Π»ΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
Π½ΠΎΠ²ΠΎΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ ΠΊΠΎΠΆΠΈ, ΠΎΠ½ΠΈ ΠΎΡΡΠ°ΡΡΡΡ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅Π΄ΠΎΡΡΡΠΏΠ½ΡΠΌΠΈ Π΄Π»Ρ ΡΠΈΡΠΎΠΊΠΎΠΉ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠΉ ΠΏΡΠ°ΠΊΡΠΈΠΊΠΈ. ΠΠ°Π»ΠΎΠ΅ ΡΠΈΡΠ»ΠΎ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ ΠΎΠ± ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠΆΠ΅ ΡΠΎΠ·Π΄Π°Π½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ° ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΠ΅Ρ ΠΎ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°Ρ
ΠΈΡ
Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΡ Π² ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΡΡ ΠΏΡΠ°ΠΊΡΠΈΠΊΡ ΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ ΡΡΡΠΈΠ½Ρ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π² Π΄Π΅ΡΠΌΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ ΠΎΠ½ΠΊΠΎΠ»ΠΎΠ³ΠΈΠΈ. ΠΠΎΡΡΡΠ΅Π±ΠΎΠ²Π°Π½Π½ΡΠΌΠΈ ΠΎΡΡΠ°ΡΡΡΡ ΠΊΠ°ΠΊ Π½ΠΎΠ²ΡΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ ΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΡΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π½Π° ΠΈΡ
ΠΎΡΠ½ΠΎΠ²Π΅, ΡΠ°ΠΊ ΠΈ ΡΠ°Π±ΠΎΡΡ, ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡΠΈΠ΅ ΠΈΡ
ΡΠΎΡΠ½ΠΎΡΡΡ Π½Π° ΡΠΎΠΏΠΎΡΡΠ°Π²ΠΈΠΌΠΎΠΌ ΠΈ ΠΏΡΠΎΠ²Π΅ΡΡΠ΅ΠΌΠΎΠΌ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π΅
Automated Remote Diagnosis of Dermatological Neoplasms
Minimum requirements for techniques of remote and automated diagnostics of skin neoplasms are formulated. For the first time, requests received by the Russian teledermatological service dermatology.ru are analyzed to determine promising directions for the development of techniques of computer analysis of skin neoplasm images. Β© 2019, Springer Science+Business Media, LLC, part of Springer Nature
Nevi in children: Organoid epidermal nevi: Clinical picture, diagnosis, treatment (Part 2)
Nevi are congenital formations that appear on the skin from birth or in early childhood, are very common in healthy children and, as a rule, are harmless. The article deals with epidermal nevi formed from epidermal cells and skin appendages (sebaceous and sweat glands, hair follicles). Particular attention is paid to the syndromes of epidermal nevi, which are characterized by a combination of skin rashes with systemic manifestations. Correct diagnosis of different subtypes of nevi, their differential diagnosis with other pigment formations (including melanomas) and non- melanoma skin cancer, as well as the recognition of non-uniform syndromes will help to determine the pediatrician correct tactics of management of patients, further counseling and assess the prognosis of the disease. Early diagnosis using dermatoscopy and modern techniques based on artificial intelligence is most significant in children before the development of progressive symptoms or neurological disorders. In the detection of epidermal nevus syndromes, consultations of related specialists (neurologists, traumatologists, cardiologists, etc.) are recommended. Β© 2020, Pediatria Ltd.. All rights reserved
Algorithm for the Analysis of Pigment Network Characteristics in Diagnosing Melanoma
Abstract: An algorithm for analyzing the characteristics of the pigment network of skin neoplasms is proposed. It is based on the assessment of the deviation coefficient of the average lengths of the pigment network segments in the local areas of the neoplasm from the average value of the lengths of the pigment network segments throughout the area of the neoplasm. The use of the algorithm makes it possible to distinguish the typical pigment network from an atypical one. An atypical pigment network is a core feature in identifying early melanoma. The algorithm can be used in automated systems to support medical decision-making in the diagnosis of skin neoplasms. Β© 2021, Pleiades Publishing, Ltd
Model for Detecting Globules in Images of Skin Neoplasms
Abstract: This article is devoted to the digital processing of images of skin neoplasms to detect significant structural elements in the diagnosis of melanomaβglobules (clumps, lumps). A new processing model is proposed, which makes it possible to stably select globules in images of different contrasts without the need to manually adjust the parameters. The results of the experiment confirming the adequacy of the model are presented. The globule recognition accuracy ranged from 81 to 89%, depending on the contrast of the original images. The experimental sample of images contained 2868 globules. Β© 2022, Pleiades Publishing, Ltd
ΠΠ΅Π»Π°Π½ΠΎΡΠΈΡΠ°ΡΠ½ΡΠΉ Π½Π΅Π²ΡΡ ΠΊΠ°ΠΊ ΠΏΡΠ΅Π΄ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΈΠΊ ΠΌΠ΅Π»Π°Π½ΠΎΠΌΡ: ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ»ΡΡΠ°ΠΈ ΠΈ ΠΎΠ±Π·ΠΎΡ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ
Most cases of malignant melanoma develop de novo; in a third of cases, however, a tumor may arise on the background of a pre-existing melanocytic nevus. Nevus associated melanomas have some epidemiological features: they develop at a younger age, are more often are located on the trunk and have a lower Breslow thickness. The available literature data and own authorsβexperience indicate the absence of the need for βprophylactic" removal of melanocytic nevi as possible precursors of a malignant tumor. One of the methods for the early diagnosis of melanoma can be the introduction of digital dermatoscopy using automated processing of dermatoscopic images with artificial intelligence. The article describes the clinical cases of nevus-associated melanomas; litreture review and discussion of this problem are presented.ΠΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²ΠΎ ΡΠ»ΡΡΠ°Π΅Π² ΠΌΠ΅Π»Π°Π½ΠΎΠΌΡ ΠΊΠΎΠΆΠΈ ΡΠ°Π·Π²ΠΈΠ²Π°ΡΡΡΡ de novo, ΠΎΠ΄Π½Π°ΠΊΠΎ Π² ΡΡΠ΅ΡΠΈ ΡΠ»ΡΡΠ°Π΅Π² Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΠΏΡΡ
ΠΎΠ»ΠΈ Π½Π° ΡΠΎΠ½Π΅ ΡΡΡΠ΅ΡΡΠ²ΠΎΠ²Π°Π²ΡΠ΅Π³ΠΎ ΡΠ°Π½Π΅Π΅ ΠΌΠ΅Π»Π°Π½ΠΎΡΠΈΡΠ°ΡΠ½ΠΎΠ³ΠΎ Π½Π΅Π²ΡΡΠ°. ΠΡΡΠΎΡΠΈΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ Ρ Π½Π΅Π²ΡΡΠΎΠΌ ΠΎΠΏΡΡ
ΠΎΠ»ΠΈ ΠΈΠΌΠ΅ΡΡ Π½Π΅ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠΏΠΈΠ΄Π΅ΠΌΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ: ΠΎΠ½ΠΈ ΡΠ°Π·Π²ΠΈΠ²Π°ΡΡΡΡ Π² Π±ΠΎΠ»Π΅Π΅ ΠΌΠΎΠ»ΠΎΠ΄ΠΎΠΌ Π²ΠΎΠ·ΡΠ°ΡΡΠ΅, ΡΠ°ΡΠ΅ ΡΠ°ΡΠΏΠΎΠ»Π°Π³Π°ΡΡΡΡ Π½Π° ΠΊΠΎΠΆΠ΅ ΡΡΠ»ΠΎΠ²ΠΈΡΠ° ΠΈ ΠΎΡΠ»ΠΈΡΠ°ΡΡΡΡ ΠΌΠ΅Π½ΡΡΠ΅ΠΉ ΡΠΎΠ»ΡΠΈΠ½ΠΎΠΉ ΠΏΠΎ ΠΡΠ΅ΡΠ»ΠΎΡ. ΠΠΌΠ΅ΡΡΠΈΠ΅ΡΡ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΈ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΎΠΏΡΡ ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΡΡ ΠΎΠ± ΠΎΡΡΡΡΡΡΠ²ΠΈΠΈ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ Β«ΠΏΡΠΎΡΠΈΠ»Π°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎΒ» ΡΠ΄Π°Π»Π΅Π½ΠΈΡ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΠΌΠ΅Π»Π°Π½ΠΎΡΠΈΡΠ°ΡΠ½ΡΡ
Π½Π΅Π²ΡΡΠΎΠ² ΠΊΠ°ΠΊ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΡ
ΠΏΡΠ΅Π΄ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΈΠΊΠΎΠ² Π·Π»ΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΎΠΏΡΡ
ΠΎΠ»ΠΈ. ΠΠ΄Π½ΠΈΠΌ ΠΈΠ· ΡΠΏΠΎΡΠΎΠ±ΠΎΠ² ΡΠ°Π½Π½Π΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΌΠ΅Π»Π°Π½ΠΎΠΌΡ ΠΊΠΎΠΆΠΈ ΠΌΠΎΠΆΠ΅Ρ ΡΡΠ°ΡΡ Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΠ΅ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ Π΄Π΅ΡΠΌΠ°ΡΠΎΡΠΊΠΎΠΏΠΈΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΄Π΅ΡΠΌΠ°ΡΠΎΡΠΊΠΎΠΏΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ°. Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ»ΡΡΠ°Π΅Π² ΠΠ, Π²ΠΎΠ·Π½ΠΈΠΊΡΠΈΡ
Π½Π° ΡΠΎΠ½Π΅ ΠΌΠ΅Π»Π°Π½ΠΎΡΠΈΡΠ°ΡΠ½ΡΡ
Π½Π΅Π²ΡΡΠΎΠ², ΠΎΠ±Π·ΠΎΡ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ ΠΈ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ Π΄Π°Π½Π½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ
Characteristics of the Structural Elements of the βLinesβ in the Melanoma Recognition System
Abstract In this study, the problem of early diagnosis of one of the most dangerous malignant neoplasms of the skinβmelanomaβis considered. A model of distinguishing structural elements (lines) in digital images of skin neoplasms in oncodermatology has been developed. The model is based on a combination of Otsu binarization and adaptive binarization of the original digital dermatoscopic image of skin neoplasms and subsequent skeletonization and filtration of false fragments of the lines