Clinical applications of artificial intelligence in radiology

Mello‐Thoms, Claudia; Mello, Carlos A. B.

doi:10.1259/bjr.20221031

Cited by 16 publications

(3 citation statements)

References 98 publications

(114 reference statements)

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“…The ability of convolutional neural networks (CNN) to recognize patterns in images for medical diagnosis has surpassed the accuracy of clinical specialists in experimental settings in various medical fields ( 2 ). To date algorithms have been successfully applied to image analysis for radiology ( 3 ), cardiology ( 4 ), ophthalmology ( 5 ), and dermatology ( 6 ). However, the translation of AI into clinical practice is still in its early stages, as researchers navigate complex issues relating to patient informed consent and privacy, representative and diverse training datasets, patient and clinician trust, explainable AI, and clinical workflow ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification

Primiero,

Betz-Stablein,

Ascott

et al. 2024

Front. Med.

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IntroductionArtificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with ‘untrained’ or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process.MethodsThis protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data.ConclusionThe anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer.

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Section: Introductionmentioning

confidence: 99%

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification

Primiero,

Betz-Stablein,

Ascott

et al. 2024

Front. Med.

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“… 1 , 2 This is set amongst an ever-increasing pressure on the radiologists’ productivity, which may result in increased risk of missed findings. 3 Within this context, it is important to maintain the quality of the radiologist’s report findings.…”

Section: Introductionmentioning

confidence: 99%

A real-world evaluation of the diagnostic accuracy of radiologists using positive predictive values verified from deep learning and natural language processing chest algorithms deployed retrospectively

Bhatia,

Morlese,

Yusuf

et al. 2023

BJR|Open

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Objectives This diagnostic study assessed the accuracy of radiologists retrospectively, using the deep learning and natural language processing chest algorithms implemented in Clinical Review version 3.2 for: pneumothorax, rib fractures in digital chest X-ray radiographs (CXR); aortic aneurysm, pulmonary nodules, emphysema, and pulmonary embolism in CT images. Methods The study design was double-blind (artificial intelligence [AI] algorithms and humans), retrospective, non-interventional, and at a single NHS Trust. Adult patients (≥18 years old) scheduled for CXR and CT were invited to enroll as participants through an opt-out process. Reports and images were de-identified, processed retrospectively, and AI-flagged discrepant findings were assigned to two lead radiologists, each blinded to patient identifiers and original radiologist. The radiologist’s findings for each clinical condition were tallied as a verified discrepancy (true positive) or not (false positive). Results The missed findings were: 0.02% rib fractures, 0.51% aortic aneurysm, 0.32% pulmonary nodules, 0.92% emphysema, and 0.28% pulmonary embolism. The positive predictive values (PPVs) were: pneumothorax (0%), rib fractures (5.6%), aortic dilatation (43.2%), pulmonary emphysema (46.0%), pulmonary embolus (11.5%), and pulmonary nodules (9.2%). The PPV for pneumothorax was nil owing to lack of available studies that were analysed for outpatient activity. Conclusions The number of missed findings was far less than generally predicted. The chest algorithms deployed retrospectively were a useful quality tool and AI augmented the radiologists’ workflow. Advances in knowledge The diagnostic accuracy of our radiologists generated missed findings of 0.02% for rib fractures CXR, 0.51% for aortic dilatation, 0.32% for pulmonary nodule, 0.92% for pulmonary emphysema, and 0.28% for pulmonary embolism for CT studies, all retrospectively evaluated with AI used as a quality tool to flag potential missed findings. It is important to account for prevalence of these chest conditions in clinical context and use appropriate clinical thresholds for decision-making, not relying solely on AI.

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“…These services allow for remote consultations, assessments, and follow-up care, facilitating access to specialized care regardless of geographic constrains. Parallel developments have been observed in other medical fields, with the proliferation of asynchronous tele-healthcare technologies (Chan et al, 2018;Hooper et al, 2001;Mahnke et al, 2008;Rotvold et al, 2003;Shapiro et al, 2004;Thrall, 2007;Yellowlees et al, 2021) and the growing applicability of artificial intelligence (AI) (Mello-Thoms & Mello, 2023;Srivastava et al, 2023;Subhan et al, 2023;Young et al, 2020). In otology, AI models have shown remarkable accuracy in classifying ear diseases through video-otoscopic images (Habib et al, 2022).…”

Section: Approaches To Address Challenges In Hearing Healthcarementioning

confidence: 99%

Remote Ear-Nose-and-Throat Specialist Screening in Adult Potential First-Time Hearing Aid Users: A Randomized Clinical Trial

Siggaard

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Clinical applications of artificial intelligence in radiology

Cited by 16 publications

References 98 publications

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification

A real-world evaluation of the diagnostic accuracy of radiologists using positive predictive values verified from deep learning and natural language processing chest algorithms deployed retrospectively

Remote Ear-Nose-and-Throat Specialist Screening in Adult Potential First-Time Hearing Aid Users: A Randomized Clinical Trial

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