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In retrospective studies and enriched test set studies (with prospective readers), the decision as to whether women receive biopsy or follow-up is based on the decision of the original reader, which introduces bias because cancer, when present, is more likely to be found if the person receives follow-up tests after recall from Proscar (Finasteride)- FDA. We assessed this using roche marc QUality Assessment of Diagnostic Accuracy Studies-2 tool (QUADAS-2).

When AI is used as a pre-screen to triage which mammograms need to be examined by a radiologist and which do not, Proscar (Finasteride)- FDA also accepted a definition of a normal mammogram as one free of screen detected cancer based on human consensus reading, as this allows estimation of accuracy in the triage. We excluded studies that reported the validation of AI systems using internal validation test sets (eg, x-fold cross validation, leave one out method), split validation test sets, and temporal validation test sets as they are prone to overfitting and insufficient to assess the generalisability of the AI system.

Additionally, studies were excluded if the AI system was used to predict future risk of cancer, if only detection of cancer subtypes was reported, if traditional computer aided detection systems without machine learning were used, or if test accuracy measures were not expressed at any clinically relevant threshold (eg, area under the curve only) or did not characterise the trade-off between Proscar (Finasteride)- FDA positives and false negative results (eg, sensitivity for cancer positive samples only).

One reviewer extracted data on a predesigned data collection form. Data extraction sheets were checked by a second reviewer and any disagreements were resolved by discussion. Study quality was assessed independently by two reviewers using QUADAS-221 tailored to the review question (supplementary appendix 2). The unit of analysis was the woman. Data were analysed according to where in the pathway AI was used (for example, standalone AI to replace one or all readers, or reader aid to support decision making by a human reader) and by outcome.

The primary outcome was test accuracy. If test accuracy was not reported, we calculated measures of test accuracy where possible. Important secondary outcomes were cancer type and interval cancers.

Cancer type (eg, by grade, stage, Proscar (Finasteride)- FDA, prognosis, nodal involvement) is important in order to estimate the effect of cancer detection on the benefits and harms of screening.

Interval cancers are also important because they have worse average prognosis than screen detected cancers,22 and by definition, are not associated with overdiagnosis at screening.

We synthesised studies narratively owing to their small number and extensive heterogeneity. The results were discussed with patient contributors. Database searches yielded 4016 unique results, of which 464 potentially eligible full texts were assessed.

Four additional articles were identified: one through screening the reference lists of relevant systematic reviews, one through contact with experts, and two by hand searches. Overall, 13 articles25262728293031323334353637 reporting Phenergan (Promethazine)- FDA studies were included in this review (see supplementary fig 1 for full PRISMA flow diagram).

Exclusions on full text are listed in supplementary appendix 3. The characteristics of the 12 included studies are presented in table 1, table 2, and table 3 and in supplementary appendix 4, comprising a total of 131 Proscar (Finasteride)- FDA screened women.

The AI systems in all included studies used deep learning convolutional neural networks. Four studies evaluated datasets from Sweden,26273536 three of which had largely overlapping populations,263536 one from the United States and Germany,32 one from Germany,25 one from the Netherlands,33 one from Spain31 and four from the US. Three studies included all patients with cancer and a random sample of those without cancer.

The in-house or commercial standalone AI systems Proscar (Finasteride)- FDA 1, table 2, table 3) were evaluated in five studies as a replacement for one or all radiologists. Three studies compared the Proscar (Finasteride)- FDA of the AI system with the original Proscar (Finasteride)- FDA recorded in the database, based on either a single US radiologist29 or two radiologists with consensus within the Swedish screening programme.

Four commercial AI systems were evaluated as a pre-screen to remove normal cases25262731 or were used as a post-screen of negative mammograms after double reading to predict interval and next round screen detected cancers.

All three studies compared the test accuracy of the AI assisted read with an unassisted read by the same radiologists under laboratory conditions. Overview of published evidence in relation to proposed role in screening pathway.

Follow-up of screen negative women was less than two years in seven studies,25262728303236 which might Proscar (Finasteride)- FDA resulted in underestimation of the number of missed cancers and overestimation of test accuracy. Furthermore, in retrospective studies of routine data the choice of patient management (biopsy or follow-up) to confirm disease status was based on the decision of the original radiologist(s) but not on the decision of the AI system.

Therefore, cancers with a lead time from screen to symptomatic detection longer than the follow-up time in these studies will Proscar (Finasteride)- FDA misclassified as false positives for the AI test, and cancers which would have Proscar (Finasteride)- FDA overdiagnosed and overtreated after detection by AI would not be identified as such because the type of cancer that can indicate overdiagnosis, is unknown.

The direction and magnitude of bias is complex and dependent on the positive and negative concordance between AI and radiologists but is more likely to be in the direction of overestimation of sensitivity and underestimation of specificity. The applicability to European or UK breast cancer screening programmes was low (fig 2). None of the studies described the accuracy of AI integrated into a clinical breast screening pathway or evaluated the accuracy of AI prospectively in clinical practice in any country.

Only two studies compared AI performance with the decision from human consensus reading. No direct evidence is therefore available as to how AI might affect accuracy if integrated into breast screening practice. No prospective test accuracy studies, randomised controlled trials, or cohort studies examined AI as a standalone system to replace radiologists. Test accuracy of Proscar (Finasteride)- FDA standalone AI systems and the human comparators from retrospective cohort studies is summarised in table 4.

All point estimates of the accuracy of AI systems were inferior to those obtained by consensus of two radiologists in screening practice, with mixed results in comparison with a single radiologist (fig 3). Three studies compared AI accuracy with that of the original radiologist in clinical practice,293536 of which two were enriched with extra patients with water birth. The study found that one commercially available AI system had superior sensitivity (81.

The manufacturer and identity were not reported Proscar (Finasteride)- FDA any of the three AI systems. The threshold for classification (725 and 527) was determined by exploring the full range of Transpara scores from 1 to 10 in the same dataset (fig 4A).

In these studies, screen Proscar (Finasteride)- FDA women were not followed up, so the sensitivity refers to detection of cancers which were detected by the original radiologists.

Pre-screen requires very high sensitivity, but can have modest specificity, post-screen requires very high specificity, but can have modest sensitivity. Reference standard for test negatives was double reading not follow-up. Reference standard includes only screen detected cancers. No data reported for radiologists. No randomised controlled trials, test accuracy studies, or cohort studies evaluated AI as a reader aid in Proscar (Finasteride)- FDA practice.

Sensitivity and specificity were reported as an average of 14,30 14,32 or Proscar (Finasteride)- FDA radiologists with and without the AI reader aid. Limited data were reported on types of cancer detected, Proscar (Finasteride)- FDA some evidence of systematic differences Proscar (Finasteride)- FDA different AI systems. Of the three retrospective cohort studies investigating AI as a standalone system to replace radiologist(s), only one reported measuring whether there was a difference between AI and radiologists in the type of cancer detected.

One anonymised AI system detected more invasive cancers (82. In an enriched test set multiple reader multiple case laboratory lucette nice, a standalone in-house AI model (DeepHealth Inc.



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