Abstract

ObjectivesAI-based algorithms for medical image analysis showed comparable performance to human image readers. However, in practice, diagnoses are made using multiple imaging modalities alongside other data sources. We determined the importance of this multi-modal information and compared the diagnostic performance of routine breast cancer diagnosis to breast ultrasound interpretations by humans or AI-based algorithms.MethodsPatients were recruited as part of a multicenter trial (NCT02638935). The trial enrolled 1288 women undergoing routine breast cancer diagnosis (multi-modal imaging, demographic, and clinical information). Three physicians specialized in ultrasound diagnosis performed a second read of all ultrasound images. We used data from 11 of 12 study sites to develop two machine learning (ML) algorithms using unimodal information (ultrasound features generated by the ultrasound experts) to classify breast masses which were validated on the remaining study site. The same ML algorithms were subsequently developed and validated on multi-modal information (clinical and demographic information plus ultrasound features). We assessed performance using area under the curve (AUC).ResultsOf 1288 breast masses, 368 (28.6%) were histopathologically malignant. In the external validation set (n = 373), the performance of the two unimodal ultrasound ML algorithms (AUC 0.83 and 0.82) was commensurate with performance of the human ultrasound experts (AUC 0.82 to 0.84; p for all comparisons > 0.05). The multi-modal ultrasound ML algorithms performed significantly better (AUC 0.90 and 0.89) but were statistically inferior to routine breast cancer diagnosis (AUC 0.95, p for all comparisons ≤ 0.05).ConclusionsThe performance of humans and AI-based algorithms improves with multi-modal information.Key Points• The performance of humans and AI-based algorithms improves with multi-modal information.• Multimodal AI-based algorithms do not necessarily outperform expert humans.• Unimodal AI-based algorithms do not represent optimal performance to classify breast masses.

Highlights

  • The use of automated medical image analysis by AI-based algorithms has generated great enthusiasm: world-class radiological evaluations may become frequently available for low-income countries, rural areas, or physicians in training [1]

  • We compared the diagnostic performance of routine breast cancer diagnosis to breast ultrasound interpretations by humans or AI-based algorithms which were trained either on unimodal information or on multi-modal information to classify breast masses

  • We developed and validated two machine learning (ML) algorithms trained on unimodal information to classify breast masses

Read more

Summary

Introduction

The use of automated medical image analysis by AI-based algorithms has generated great enthusiasm: world-class radiological evaluations may become frequently available for low-income countries, rural areas, or physicians in training [1]. Algorithms for medical image analysis are developed either by using hand-crafted image features (extracted automatically or by human readers) that are analyzed by machine learning algorithms or by using deep learning techniques that do not require prior feature extraction [1]. Such algorithms have already shown great diagnostic performance comparable to human expert readers in some areas [3]. The discrepancy between the excellent performance reported by newly developed imaging algorithms and their non-use in clinical practice as well as the reluctance expressed by human imaging experts seems striking. An explanation for this may be that algorithms which are trained on image data alone may perform on par with human image readers when looking only at those images — but this does not represent the clinical reality in which imaging information (of multiple imaging modalities) is often considered alongside contextualizing clinical and demographic information

Objectives
Methods
Results
Discussion
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.