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1.
Sci Data ; 8(1): 34, 2021 01 28.
Artigo em Inglês | MEDLINE | ID: mdl-33510154

RESUMO

Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.


Assuntos
Melanoma , Neoplasias Cutâneas , Inteligência Artificial , Humanos , Melanoma/diagnóstico por imagem , Melanoma/patologia , Melanoma/fisiopatologia , Metadados , Pele/patologia , Neoplasias Cutâneas/diagnóstico por imagem , Neoplasias Cutâneas/patologia , Neoplasias Cutâneas/fisiopatologia
2.
J Am Acad Dermatol ; 82(3): 622-627, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31306724

RESUMO

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.


Assuntos
Aprendizado Profundo , Dermoscopia/métodos , Interpretação de Imagem Assistida por Computador/métodos , Melanoma/diagnóstico , Neoplasias Cutâneas/diagnóstico , Colômbia , Estudos Transversais , Dermatologistas/estatística & dados numéricos , Dermoscopia/estatística & dados numéricos , Diagnóstico Diferencial , Humanos , Cooperação Internacional , Internato e Residência/estatística & dados numéricos , Israel , Ceratose Seborreica/diagnóstico , Melanoma/patologia , Nevo/diagnóstico , Curva ROC , Pele/diagnóstico por imagem , Pele/patologia , Neoplasias Cutâneas/patologia , Espanha , Estados Unidos
3.
Lancet Oncol ; 20(7): 938-947, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31201137

RESUMO

BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. METHODS: For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen's disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms. FINDINGS: Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55·4%) of 511 human readers were board-certified dermatologists, 118 (23·1%) were dermatology residents, and 83 (16·2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2·01 (95% CI 1·97 to 2·04; p<0·0001) more correct diagnoses (17·91 [SD 3·42] vs 19·92 [4·27]). 27 human experts with more than 10 years of experience achieved a mean of 18·78 (SD 3·15) correct answers, compared with 25·43 (1·95) correct answers for the top three machine algorithms (mean difference 6·65, 95% CI 6·06-7·25; p<0·0001). The difference between human experts and the top three algorithms was significantly lower for images in the test set that were collected from sources not included in the training set (human underperformance of 11·4%, 95% CI 9·9-12·9 vs 3·6%, 0·8-6·3; p<0·0001). INTERPRETATION: State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice. However, a possible limitation of these algorithms is their decreased performance for out-of-distribution images, which should be addressed in future research. FUNDING: None.


Assuntos
Algoritmos , Dermoscopia , Internet , Aprendizado de Máquina , Transtornos da Pigmentação/patologia , Neoplasias Cutâneas/patologia , Adulto , Feminino , Humanos , Masculino , Reprodutibilidade dos Testes , Estudos Retrospectivos
4.
Tomography ; 4(1): 33-41, 2018 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-29984312

RESUMO

A challenge in multicenter trials that use quantitative positron emission tomography (PET) imaging is the often unknown variability in PET image values, typically measured as standardized uptake values, introduced by intersite differences in global and resolution-dependent biases. We present a method for the simultaneous monitoring of scanner calibration and reconstructed image resolution on a per-scan basis using a PET/computed tomography (CT) "pocket" phantom. We use simulation and phantom studies to optimize the design and construction of the PET/CT pocket phantom (120 × 30 × 30 mm). We then evaluate the performance of the PET/CT pocket phantom and accompanying software used alongside an anthropomorphic phantom when known variations in global bias (±20%, ±40%) and resolution (3-, 6-, and 12-mm postreconstruction filters) are introduced. The resulting prototype PET/CT pocket phantom design uses 3 long-lived sources (15-mm diameter) containing germanium-68 and a CT contrast agent in an epoxy matrix. Activity concentrations varied from 30 to 190 kBq/mL. The pocket phantom software can accurately estimate global bias and can detect changes in resolution in measured phantom images. The pocket phantom is small enough to be scanned with patients and can potentially be used on a per-scan basis for quality assurance for clinical trials and quantitative PET imaging in general. Further studies are being performed to evaluate its performance under variations in clinical conditions that occur in practice.

5.
J Am Acad Dermatol ; 78(2): 270-277.e1, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-28969863

RESUMO

BACKGROUND: Computer vision may aid in melanoma detection. OBJECTIVE: We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images. METHODS: We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into "fusion" algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant. RESULTS: The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001). LIMITATIONS: The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice. CONCLUSION: Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists.


Assuntos
Algoritmos , Dermatologistas , Dermoscopia , Lentigo/diagnóstico por imagem , Melanoma/diagnóstico , Nevo/diagnóstico por imagem , Neoplasias Cutâneas/diagnóstico por imagem , Congressos como Assunto , Estudos Transversais , Diagnóstico por Computador , Humanos , Aprendizado de Máquina , Melanoma/patologia , Curva ROC , Neoplasias Cutâneas/patologia
6.
J Med Imaging (Bellingham) ; 3(3): 035505, 2016 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-27660808

RESUMO

To address the error introduced by computed tomography (CT) scanners when assessing volume and unidimensional measurement of solid tumors, we scanned a precision manufactured pocket phantom simultaneously with patients enrolled in a lung cancer clinical trial. Dedicated software quantified bias and random error in the [Formula: see text], and [Formula: see text] dimensions of a Teflon sphere and also quantified response evaluation criteria in solid tumors and volume measurements using both constant and adaptive thresholding. We found that underestimation bias was essentially the same for [Formula: see text], and [Formula: see text] dimensions using constant thresholding and had similar values for adaptive thresholding. The random error of these length measurements as measured by the standard deviation and coefficient of variation was 0.10 mm (0.65), 0.11 mm (0.71), and 0.59 mm (3.75) for constant thresholding and 0.08 mm (0.51), 0.09 mm (0.56), and 0.58 mm (3.68) for adaptive thresholding, respectively. For random error, however, [Formula: see text] lengths had at least a fivefold higher standard deviation and coefficient of variation than [Formula: see text] and [Formula: see text]. Observed [Formula: see text]-dimension error was especially high for some 8 and 16 slice CT models. Error in CT image formation, in particular, for models with low numbers of detector rows, may be large enough to be misinterpreted as representing either treatment response or disease progression.

7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2016: 1361-1364, 2016 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-28268578

RESUMO

This paper presents a robust segmentation method based on multi-scale classification to identify the lesion boundary in dermoscopic images. Our proposed method leverages a collection of classifiers which are trained at various resolutions to categorize each pixel as "lesion" or "surrounding skin". In detection phase, trained classifiers are applied on new images. The classifier outputs are fused at pixel level to build probability maps which represent lesion saliency maps. In the next step, Otsu thresholding is applied to convert the saliency maps to binary masks, which determine the border of the lesions. We compared our proposed method with existing lesion segmentation methods proposed in the literature using two dermoscopy data sets (International Skin Imaging Collaboration and Pedro Hispano Hospital) which demonstrates the superiority of our method with Dice Coefficient of 0.91 and accuracy of 94%.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Neoplasias Cutâneas/diagnóstico por imagem , Pele/diagnóstico por imagem , Algoritmos , Bases de Dados Factuais , Dermoscopia/métodos , Humanos , Aprendizado de Máquina , Nevo/diagnóstico por imagem , Nevo/patologia , Pele/patologia , Neoplasias Cutâneas/patologia
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