#AI approach outperformed human experts (AGAIN) in identifying #cervical precancer!
A research team led by investigators from the National Institutes of Health and Global Good has developed a #deeplearning #algorithm that can analyze digital images of a woman's cervix and accurately identify precancerous changes that require medical attention. This artificial intelligence (AI) approach, called automated visual evaluation, has the potential to revolutionize cervical cancer screening, particularly in low-resource settings.
To create the algorithm, the research team used more than 60,000 cervical images from an NCI archive of photos collected during a cervical cancer screening study that was carried out in Costa Rica in the 1990s.
Overall, the algorithm performed better than all standard screening tests at predicting all cases diagnosed during the Costa Rica study. Automated visual evaluation identified precancer with greater accuracy (AUC=0.91) than a human expert review (AUC=0.69) or conventional cytology (AUC=0.71).
Paper here: https://lnkd.in/dxETi8K
#algorithms #prediction #cancer #machinelearning #cnn #transferlearning
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv
A research team led by investigators from the National Institutes of Health and Global Good has developed a #deeplearning #algorithm that can analyze digital images of a woman's cervix and accurately identify precancerous changes that require medical attention. This artificial intelligence (AI) approach, called automated visual evaluation, has the potential to revolutionize cervical cancer screening, particularly in low-resource settings.
To create the algorithm, the research team used more than 60,000 cervical images from an NCI archive of photos collected during a cervical cancer screening study that was carried out in Costa Rica in the 1990s.
Overall, the algorithm performed better than all standard screening tests at predicting all cases diagnosed during the Costa Rica study. Automated visual evaluation identified precancer with greater accuracy (AUC=0.91) than a human expert review (AUC=0.69) or conventional cytology (AUC=0.71).
Paper here: https://lnkd.in/dxETi8K
#algorithms #prediction #cancer #machinelearning #cnn #transferlearning
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv
Really awesome #deeplearning #rnn paper 👏🏻 explaining an increase in predicted risk for clinical alerts.
Explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step.
The goal here is to alert a clinician when a patient’s risk of deterioration rises. The clinician then has to decide whether to intervene and adjust the treatment.
Given a potentially long sequence of new events since she last saw the patient, a concise explanation helps her to quickly triage the alert.
Authors developed methods to lift static attribution techniques to the dynamical setting, where they identified and addressed challenges specific to dynamics.
They then experimentally assess the utility of different explanations of clinical alerts through expert evaluation.
Here’s full paper: https://lnkd.in/duMQYyW
#healthcare #diagnostics #clinical
#prediction
✴️ @AI_Python_EN
Explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step.
The goal here is to alert a clinician when a patient’s risk of deterioration rises. The clinician then has to decide whether to intervene and adjust the treatment.
Given a potentially long sequence of new events since she last saw the patient, a concise explanation helps her to quickly triage the alert.
Authors developed methods to lift static attribution techniques to the dynamical setting, where they identified and addressed challenges specific to dynamics.
They then experimentally assess the utility of different explanations of clinical alerts through expert evaluation.
Here’s full paper: https://lnkd.in/duMQYyW
#healthcare #diagnostics #clinical
#prediction
✴️ @AI_Python_EN