•Active learning: to choose the best data to annotate for optimal model performance.•Interpretation + Refinement: feedback for a prediction, meaningful ways to respond.•Practical considerations: full ...scale applications and considerations for deployment.•Related Areas: evolving research fields to benefit human-in-the-loop computing.
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Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy. However, the unique challenges posed by medical image analysis suggest that retaining a human end-user in any deep learning enabled system will be beneficial. In this review we investigate the role that humans might play in the development and deployment of deep learning enabled diagnostic applications and focus on techniques that will retain a significant input from a human end user. Human-in-the-Loop computing is an area that we see as increasingly important in future research due to the safety-critical nature of working in the medical domain. We evaluate four key areas that we consider vital for deep learning in the clinical practice: (1) Active Learning to choose the best data to annotate for optimal model performance; (2) Interaction with model outputs - using iterative feedback to steer models to optima for a given prediction and offering meaningful ways to interpret and respond to predictions; (3) Practical considerations - developing full scale applications and the key considerations that need to be made before deployment; (4) Future Prospective and Unanswered Questions - knowledge gaps and related research fields that will benefit human-in-the-loop computing as they evolve. We offer our opinions on the most promising directions of research and how various aspects of each area might be unified towards common goals.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
A square-free monomial ideal
$I$
of
$kx_{1},\ldots ,x_{n}$
is said to be an
$f$
-ideal if the facet complex and non-face complex associated with
$I$
have the same
$f$
-vector. We show that
$I$
is an
...$f$
-ideal if and only if its Newton complementary dual
$\widehat{I}$
is also an
$f$
-ideal. Because of this duality, previous results about some classes of
$f$
-ideals can be extended to a much larger class of
$f$
-ideals. An interesting by-product of our work is an alternative formulation of the Kruskal–Katona theorem for
$f$
-vectors of simplicial complexes.
Background
Artificial intelligence (AI) has the potential to improve prenatal detection of congenital heart disease. We analysed the performance of the current national screening programme in ...detecting hypoplastic left heart syndrome (HLHS) to compare with our own AI model.
Methods
Current screening programme performance was calculated from local and national sources. AI models were trained using four‐chamber ultrasound views of the fetal heart, using a ResNet classifier.
Results
Estimated current fetal screening programme sensitivity and specificity for HLHS were 94.3% and 99.985%, respectively. Depending on calibration, AI models to detect HLHS were either highly sensitive (sensitivity 100%, specificity 94.0%) or highly specific (sensitivity 93.3%, specificity 100%). Our analysis suggests that our highly sensitive model would generate 45,134 screen positive results for a gain of 14 additional HLHS cases. Our highly specific model would be associated with two fewer detected HLHS cases, and 118 fewer false positives.
Conclusion
If used independently, our AI model performance is slightly worse than the performance level of the current screening programme in detecting HLHS, and this performance is likely to deteriorate further when used prospectively. This demonstrates that collaboration between humans and AI will be key for effective future clinical use.
Key points
What is already known on this topic?
Artificial intelligence (AI) can be used to interpret medical images and make diagnoses, including detecting fetal congenital heart disease (CHD) by ultrasound.
The sensitivity of the current English screening programme for fetal cardiac malformations is publicly available, but specificity is not reported.
What this study adds?
The current screening programme in our region is operating at a very high specificity for fetal hypoplastic left heart syndrome (HLHS).
Using a curated retrospective dataset, it is possible to train AI models to detect HLHS with a performance approaching that of the current screening programme.
Current AI models do not have high enough specificity to be used independently for screening for fetal CHD, meaning that human‐AI interaction when performing or interpreting ultrasound will be important to select cases for specialist referral.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Objective
Advances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end‐to‐end automation of the mid‐trimester screening ultrasound scan using ...AI‐enabled tools.
Methods
A prospective method comparison study was conducted. Participants had both standard and AI‐assisted US scans performed. The AI tools automated image acquisition, biometric measurement, and report production. A feedback survey captured the sonographers' perceptions of scanning.
Results
Twenty‐three subjects were studied. The average time saving per scan was 7.62 min (34.7%) with the AI‐assisted method (p < 0.0001). There was no difference in reporting time. There were no clinically significant differences in biometric measurements between the two methods. The AI tools saved a satisfactory view in 93% of the cases (four core views only), and 73% for the full 13 views, compared to 98% for both using the manual scan. Survey responses suggest that the AI tools helped sonographers to concentrate on image interpretation by removing disruptive tasks.
Conclusion
Separating freehand scanning from image capture and measurement resulted in a faster scan and altered workflow. Removing repetitive tasks may allow more attention to be directed identifying fetal malformation. Further work is required to improve the image plane detection algorithm for use in real time.
Key points
What is already known about this topic?
Artificial intelligence has shown great promise in medical diagnosis, including in antenatal settings
Most published work has been based on retrospective data, with very little work exploring how AI might be used in real‐life clinical practice
What does this study add?
We have shown that real time use of AI in obstetric ultrasound scanning is feasible and can fundamentally disrupt how sonographers perform the scan
AI‐assisted scans were significantly faster than standard manual scans
Automatically measured fetal biometry was highly accurate
The performance of automatic standard plane acquisition needs to be improved before these tools can enter mainstream clinical use
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Abstract
A square-free monomial ideal
$I$
of
$kx_{1},\ldots ,x_{n}$
is said to be an
$f$
-ideal if the facet complex and non-face complex associated with
$I$
have the same
$f$
-vector. We show that
...$I$
is an
$f$
-ideal if and only if its Newton complementary dual
$\widehat{I}$
is also an
$f$
-ideal. Because of this duality, previous results about some classes of
$f$
-ideals can be extended to a much larger class of
$f$
-ideals. An interesting by-product of our work is an alternative formulation of the Kruskal–Katona theorem for
$f$
-vectors of simplicial complexes.
Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful ...information for computer-aided detection, diagnosis, treatment planning, intervention and therapy. However, the unique challenges posed by medical image analysis suggest that retaining a human end user in any deep learning enabled system will be beneficial. In this review we investigate the role that humans might play in the development and deployment of deep learning enabled diagnostic applications and focus on techniques that will retain a significant input from a human end user. Human-in-the-Loop computing is an area that we see as increasingly important in future research due to the safety-critical nature of working in the medical domain. We evaluate four key areas that we consider vital for deep learning in the clinical practice: (1) Active Learning to choose the best data to annotate for optimal model performance; (2) Interaction with model outputs - using iterative feedback to steer models to optima for a given prediction and offering meaningful ways to interpret and respond to predictions; (3) Practical considerations - developing full scale applications and the key considerations that need to be made before deployment; (4) Future Prospective and Unanswered Questions - knowledge gaps and related research fields that will benefit human-in-the-loop computing as they evolve. We offer our opinions on the most promising directions of research and how various aspects of each area might be unified towards common goals.
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