Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology

Daneshjou, Roxana; Barata, Catarina; Betz‐Stablein, Brigid; Celebi, M. Emre; Codella, Noel; Combalia, Marc; Guitera, Pascale; Gutman, David; Halpern, Allan C.; Helba, Brian; Kittler, Harald; Kose, Katsumi; Liopyris, Konstantinos; Malvehy, Josep; Seog, Han Seung; Soyer, H. Peter; Tkaczyk, Eric R.; Tschandl, Philipp; Rotemberg, Veronica

doi:10.1001/jamadermatol.2021.4915

Cited by 90 publications

(75 citation statements)

References 61 publications

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“…We adopted the modified 25-item CLEAR statement 23 to evaluate the reporting quality (ie, feasibility) of the included studies, which included 4 domains (data, technique, technical assessment, and applications). To ensure applicability to neuroimage-based AI models, we removed 3 items (eMethods 5 in Supplement 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…To this end, we used the Prediction Model Risk of Bias Assessment Tool (PROBAST) and modified Checklist for Evaluation of Image-Based Artificial Intelligence Reports (CLEAR) benchmarks. 23 , 24 …”

Section: Introductionmentioning

confidence: 99%

“…In this study, we sought to systematically assess the ROB and reporting quality of neuroimaging-based AI models for psychiatric diagnosis that were developed in the past 3 decades. To this end, we used the Prediction Model Risk of Bias Assessment Tool (PROBAST) and modified Checklist for Evaluation of Image-Based Artificial Intelligence Reports (CLEAR) benchmarks …”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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ImportanceNeuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.ObjectiveTo systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.Evidence ReviewPubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.FindingsA total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).Conclusions and RelevanceThis systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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“…Image processing techniques in applications rely heavily on image extraction input devices, and, with the widespread entry of cloud surveillance cameras into the market, a wide variety of image monitoring systems have been further popularized. is not only makes it possible to control and identify specific targets in complex environments but also makes the research content of image matching and target recognition techniques in computer vision more meaningful in practice [21].…”

Section: Image Processing and Recognition Algorithmsmentioning

confidence: 99%

Image Processing and Recognition Algorithm Design in Intelligent Imaging Device System

2022

Security and Communication Networks

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People may quickly employ imagig devices to acquire and use image data thanks to the rapid development of computer networks and communication technologies. However, imaging devices obtain massive data through real-time acquisition, and a large number of invalid images affect the imaging device system’s endurance on the one hand while also requiring a significant amount of time for analysis on the other hand, so there is a critical need to find a way to automate the mining of valuable information in the data. In this paper, we propose an intelligent imaging device system, which embeds a target intelligent recognition algorithm, improves the YOLOv3 model by using a method based on depth-separable convolutional blocks and inverse feature fusion structure, and finally achieves fast target detection while improving detection accuracy through the design of distance-based nonextreme suppression and loss function. By preprocessing the images and automatically identifying and saving images containing target animals, the range of the imaging device system equipment can be improved and the workload of researchers searching for target animals in images can be reduced. In this paper, we propose a method for intelligent preservation of contained target images by deploying lightweight target recognition algorithms on edge computing hardware with the intelligence of the intelligent imaging device system as the research goal. After simulation experiments, the use of this method can improve the endurance of the imaging device system and reduce the time of manual processing at a later stage.

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“…Meanwhile, the application of artificial intelligence (AI) is accelerating in medical cosmetology and has the potential to transform dermatology workflows with its applications in image recognition through utilizing machine learning, convolutional neural networks, and so on. It has great potential for patient care in medical cosmetology, particularly in improving the sensitivity and accuracy of skin lesion screening ( Hogarty et al, 2019 ; Daneshjou et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Emerging High-Frequency Ultrasound Imaging in Medical Cosmetology

Yaping

et al. 2022

Front. Physiol.

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Cosmetic skin diseases are a part of many dermatological concerns brought up by patients, which negatively affect mental health and quality of life. Imaging technology has an established role in the diagnosis of cosmetic skin diseases by recognizing information on deep skin lesions. Due to the complex physiological and pathological nature of cosmetic skin diseases, the diagnostic imaging performance varies greatly. Developing noninvasive technology models with wide applicability, particularly high-frequency ultrasound (HFUS), which is able to achieve high-resolution imaging of the skin from the stratum corneum down to the deep fascia, is of great significance to medical cosmetology. To explore the great potential of HFUS in cosmetic skin diseases, a narrative review of literature from PubMed and Web of Science published between 1985 and 2022 was conducted. This narrative review focuses on the progression of HFUS imaging in medical cosmetology, especially on its promising application in the quantitative evaluation and differential diagnosis of cutaneous pathological scar, port wine stain (PWS), acne, skin aging, and other cosmetic applications.

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Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology

Cited by 90 publications

References 61 publications

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

Image Processing and Recognition Algorithm Design in Intelligent Imaging Device System

Emerging High-Frequency Ultrasound Imaging in Medical Cosmetology

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