ID Consecutivo : 2174

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Resumen
Background: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain. Objective: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma. Methods: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level. Results: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%. Limitations: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata. Conclusion: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.

 

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Idioma Español Inglés Portugués Francés Alemán

 

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Coleccion ASCOFAME: Investigacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

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	Clínicos	Educadores Médicos	Otros profesionales de la salud
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ID Consecutivo : 2174

Titulo

Editorial

 

Año de publicación Edición Primera Segunda Tercera Cuarta Quita Sexta Septima Octava Novena Decima Selecione una

Paginas Existencias 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Resumen
Background: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain. Objective: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma. Methods: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level. Results: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%. Limitations: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata. Conclusion: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.

 

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Idioma Español Inglés Portugués Francés Alemán

 

Tipo de Acceso

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Libre

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Palabras clave

Coleccion ASCOFAME: Investigacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

Disponibilidad:	Digital	Físico	Físico y Digital	Link Externo

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ID Consecutivo : 2174

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Tipo : Ley Decreto Norma Resolucion Circular Directiva

 

Año de publicación

Resumen
Background: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain. Objective: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma. Methods: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level. Results: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%. Limitations: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata. Conclusion: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.

 

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Idioma Español Inglés Portugués Francés Alemán

 

Tipo de Acceso

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Libre

Registrado

Palabras clave

Coleccion ASCOFAME: Investigacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

Disponibilidad:	Digital	Físico	Físico y Digital	Link Externo

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	Evaluadores

 

ID Consecutivo : 2174

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Año de publicación

Resumen
Background: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain. Objective: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma. Methods: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level. Results: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%. Limitations: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata. Conclusion: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.

 

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Idioma Español Inglés Portugués Francés Alemán

 

Tipo de Acceso

Restringido(ASCOFAME)

Libre

Registrado

Palabras clave

Coleccion ASCOFAME: Investigacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

Disponibilidad:	Digital	Físico	Físico y Digital	Link Externo

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ID Consecutivo : 2174

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Resumen
Background: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain. Objective: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma. Methods: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level. Results: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%. Limitations: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata. Conclusion: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.

 

Adjunte la portada del libro Si no tiene una imagen, descargue aqui la imagen de Ascofame

Idioma Español Inglés Portugués Francés Alemán

 

Tipo de Acceso

Restringido(ASCOFAME)

Libre

Registrado

Palabras clave

Coleccion ASCOFAME: Investigacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

Disponibilidad:	Digital	Físico	Físico y Digital	Link Externo

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Publico	Pregrado	Posgrado	Decanatura
Objetivo:	Docentes	Médicos	Investigadores
	Clínicos	Educadores Médicos	Otros profesionales de la salud
	Evaluadores

 

ID Consecutivo : 2174

Titulo videos

Año de publicación

Resumen
Background: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain. Objective: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma. Methods: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level. Results: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%. Limitations: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata. Conclusion: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.

 

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Coleccion ASCOFAME: Investigacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

https://pubmed.ncbi.nlm.nih.gov/31306724/

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Publico	Pregrado	Posgrado	Decanatura
Objetivo:	Docentes	Médicos	Investigadores
	Clínicos	Educadores Médicos	Otros profesionales de la salud
	Evaluadores

 

ID Consecutivo : 2174

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Resumen
Background: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain. Objective: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma. Methods: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level. Results: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%. Limitations: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata. Conclusion: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.

 

Base de datos bibliográfica Base de datos bibliográfica Seleccione una Access Medicine Biblioteca VirtualenSalud(BVS) Cochrane Central Dialnet Lilacs-Literatura Latinoamericana en Ciencias de la Salud Medline-PubMed Redalyc ScienceDirect SciELO-Scientific Electronic Library Online WHOLIS Otra

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Idioma Español Inglés Portugués Francés Alemán

 

Tipo de Acceso

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Libre

Registrado

Palabras clave

Coleccion ASCOFAME: Investigacion Desarrollo Profesional continuo Gestion curricular Calidad y acreditacion Evaluacion Investigacion Desarrollo Profesional

Disponibilidad:	Digital	Físico	Físico y Digital	Link Externo

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Publico	Pregrado	Posgrado	Decanatura
Objetivo:	Docentes	Médicos	Investigadores
	Clínicos	Educadores Médicos	Otros profesionales de la salud
	Evaluadores