Convolutional neural network assistance significantly improves dermatologists’ diagnosis of cutaneous tumours using clinical images

Ba, Wei; Wu, Huan; Chen, Wei W.; Wang, Shu H; Zhang, Zi Y; Wei, Xuan J; Wang, Wen J.; Yang, Lei; Zhou, Dong M; Zhuang, Yi X; Zhong, Qingwen; Song, Zhi G; Li, Cheng X.

doi:10.1016/j.ejca.2022.04.015

Cited by 16 publications

(12 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… AUC 0.933 Sn 80.24 ± 3.11% Sp 91.57 ± 2.66% Acc 84.97 ± 2.45% BCC diagnosis -Dermatologists: Sn 45.98% ± 21.21 Sp 96.03% ± 6.52 Acc 65% ± 11.7 -Non-dermatologists: Sn 10.71% ±10.53 Sp 98.47% ±3.19 Acc 47.57% ± 6.32 Agarwala et al 50 Public: Triage tool www.triage.com free online system composed of four CNN models (training) Institutional (test) Training: > 200,000 images, > 500 skin conditions Test: 353 images E R B Y 21 US board-certified dermatologists Triage algorithm Multiclass Accuracy of the dermatologist’s was better than the AI accuracy Acc 63.3%; 95% CI 58.0–68.4%) Acc: 69.1% (95% CI 63.7–74.1) Kim et al 51 Public Pre-trained algorithm Institutional: Department of Dermatology, Asan Medical Center, Seoul National University, Bundang Hospital (Test) Training: 721,749 images, 178 disease classes Test: 285 images E P B N -10 attending physicians (11.4 ± 8.8 years’ experience after board certification) -11 dermatology trainees -7 intern doctors Model Dermatology; https://modelderm.com Multiclass There was no direct comparison between AI and clinicians Top-1 of the algorithm Sn 52.2% Sp 93.4% Acc 53.5% Top-2 of the algorithm Sn 69.6% Sp 78.5% Acc 66.0% Top-3 of the algorithm Sn 78.3% Sp 66.1% Acc 70.8% Top-1 Dermatologist Sn 79.3% Sp 90.2% Acc 61.8% Trainees Sn 65.5% Sp 81.3% Acc 46.5% Top-2 Dermatologist Sn 86.2% Sp 82.1% Acc 69.4% Trainees Sn 93.1% Sp 51.8% Acc 54.2% Top-3 Dermatologist Sn 86.2% Sp 79.5% Acc 71.5% Trainees Sn 93,1% Sp 49.1% Acc 54.9% Top-1/Top-2/Top-3 accuracies after assistance were significantly higher than those before assistance AI augmented the diagnostic accuracy of trainee doctors Ba. et al 41 Institutional: Chinese PLA General Hospital & Medical School Dataset: 29,280 Training/validation: 25,773. 10 categories of cutaneous tumors Test: 400 from 2107 images dataset.…”

Section: Resultsmentioning

confidence: 99%

A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis

Salinas,

Sepúlveda,

Hidalgo

et al. 2024

npj Digit. Med.

View full text Add to dashboard Cite

Scientific research of artificial intelligence (AI) in dermatology has increased exponentially. The objective of this study was to perform a systematic review and meta-analysis to evaluate the performance of AI algorithms for skin cancer classification in comparison to clinicians with different levels of expertise. Based on PRISMA guidelines, 3 electronic databases (PubMed, Embase, and Cochrane Library) were screened for relevant articles up to August 2022. The quality of the studies was assessed using QUADAS-2. A meta-analysis of sensitivity and specificity was performed for the accuracy of AI and clinicians. Fifty-three studies were included in the systematic review, and 19 met the inclusion criteria for the meta-analysis. Considering all studies and all subgroups of clinicians, we found a sensitivity (Sn) and specificity (Sp) of 87.0% and 77.1% for AI algorithms, respectively, and a Sn of 79.78% and Sp of 73.6% for all clinicians (overall); differences were statistically significant for both Sn and Sp. The difference between AI performance (Sn 92.5%, Sp 66.5%) vs. generalists (Sn 64.6%, Sp 72.8%), was greater, when compared with expert clinicians. Performance between AI algorithms (Sn 86.3%, Sp 78.4%) vs expert dermatologists (Sn 84.2%, Sp 74.4%) was clinically comparable. Limitations of AI algorithms in clinical practice should be considered, and future studies should focus on real-world settings, and towards AI-assistance.

show abstract

Section: Resultsmentioning

confidence: 99%

A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis

Salinas,

Sepúlveda,

Hidalgo

et al. 2024

npj Digit. Med.

View full text Add to dashboard Cite

show abstract

“…It covers ten types of skin cancer: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), including keratoacanthoma, melanoma (MM), Bowen disease (BD), actinic keratosis (AK), melanocytic naevus (MN), seborrhoeic keratosis (SK), haemangioma, including pyogenic granuloma, cherry haemangioma, sinusoidal haemangioma and angiokeratoma, dermatofibroma (DF) and wart. CNN used in [39] achieved an overall accuracy of 78.45%, and CNNassisted dermatologists achieved greater accuracy (76.60% versus 62.78%) than nonassisted dermatologists in interpreting clinical images.…”

Section: Related Workmentioning

confidence: 96%

“…Ba et al [39] proposed a multi-class CNN trained and validated using a dataset of 25,773 clinical images approved by the Chinese PLA General Hospital & Medical School's Institutional Review Board. It covers ten types of skin cancer: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), including keratoacanthoma, melanoma (MM), Bowen disease (BD), actinic keratosis (AK), melanocytic naevus (MN), seborrhoeic keratosis (SK), haemangioma, including pyogenic granuloma, cherry haemangioma, sinusoidal haemangioma and angiokeratoma, dermatofibroma (DF) and wart.…”

Section: Related Workmentioning

confidence: 99%

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the Clinical Images Melanoma Detection Problem

Biasi

Marco

Citarella

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Melanoma is one of the deadliest tumors in the world. Early detection is critical for first-line therapy in this tumor pathology and it remains challenging due to the need for histological analysis to ensure correctness in diagnosis. Therefore, multiple computer-aided diagnosis (CAD) systems working on melanoma images were proposed to mitigate the need of a biopsy. However, although the high global accuracy is declared in literature results, the CAD systems for the health fields must focus on the lowest false negative rate (FNR) possible to qualify as a diagnosis support system. The final goal must be to avoid classification type 2 errors to prevent life-threatening situations. Another goal could be to create an easy-to-use system for both physicians and patients. Results: To achieve the minimization of type 2 error, we performed a wide exploratory analysis of the principal convolutional neural network (CNN) architectures published for the multiple image classification problem; we adapted these networks to the melanoma clinical image binary classification problem (MCIBCP). We collected and analyzed performance data to identify the best CNN architecture, in terms of FNR, usable for solving the MCIBCP problem. Then, to provide a starting point for an easy-to-use CAD system, we used a clinical image dataset (MED-NODE) because clinical images are easier to access: they can be taken by a smartphone or other hand-size devices. Despite the lower resolution than dermoscopic images, the results in the literature would suggest that it would be possible to achieve high classification performance by using clinical images. In this work, we used MED-NODE, which consists of 170 clinical images (70 images of melanoma and 100 images of naevi). We optimized the following CNNs for the MCIBCP problem: Alexnet, DenseNet, GoogleNet Inception V3, GoogleNet, MobileNet, ShuffleNet, SqueezeNet, and VGG16. Conclusions: The results suggest that a CNN built on the VGG or AlexNet structure can ensure the lowest FNR (0.07) and (0.13), respectively. In both cases, discrete global performance is ensured. 73% (accuracy), 82% (sensitivity) and 59% (specificity) for VGG; 89% (accuracy), 87% (sensitivity) and 90% (specificity) for AlexNet.

show abstract

“…Moreover, these tools can be used to establish the malignant potential and select molecular targets for skin tumours (e.g., diagnosis and therapy of melanoma), 3 or characterize the genomic and proteomic expression of rare and common genetic dermatoses, among many other potential applications 4 . It is likely that artificial intelligence will become an indispensable ally for dermatologists in the upcoming years 5 …”

mentioning

confidence: 99%

“…4 It is likely that artificial intelligence will become an indispensable ally for dermatologists in the upcoming years. 5 Regardless of what kind of diagnostic tools will be at our disposal in the future, I am convinced that the dermatologist's own eyes will still remain the most important among these. However, dermatologists certainly need to be trained about the new possibilities for diagnosing and treating dermatologic diseases that stateof-the-art technology offers and they need to keep themselves up to date on new developments in the field.…”

mentioning

confidence: 99%

Precision diagnostics in clinical dermatology

Torrelo

2022

JEADV Clinical Practice

View full text Add to dashboard Cite

Convolutional neural network assistance significantly improves dermatologists’ diagnosis of cutaneous tumours using clinical images

Cited by 16 publications

References 35 publications

A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis

A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the Clinical Images Melanoma Detection Problem

Precision diagnostics in clinical dermatology

Contact Info

Product

Resources

About