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This study evaluated the performance of automated machine-learning to diagnose non-alcoholic fatty liver disease (NAFLD) by ultrasound and compared these findings to radiologist performance.Methods: 96 patients with histologic (33) or proton density fat fraction MRI (63) diagnosis of NAFLD and 100 patients without evidence of NAFLD were retrospectively identified. The "Fatty Liver" label included 96 patients with 405 images and the "Not Fatty Liver" label included 100 patients with 500 images. These 905 images made up a "Comprehensive Image" group.A "Radiology Selected Image" group was then created by selecting only images considered diagnostic by a blinded radiologist, resulting in 649 images. Cloud AutoML Visionbeta (Google LLC, Mountain View, CA) was used for machine learning. The models were evaluated against three blinded radiologists. Results:The "Comprehensive Image" group model demonstrated a sensitivity of 88.6% (73.3-96.8%) and a specificity of 95.3% (84.2-99.4%). Radiologist performance on this image group included a sensitivity of 81.0% (74.3-87.6%) and specificity of 86.0% (72.6-99.5%). The model's overall accuracy was 92.3% (84.0-97.1%), compared with mean individual performance (83.8%, 78.4-89.1%). The "Radiology Selected Image" group model demonstrated a sensitivity of 88.6% (73.3 -96.8%) and specificity of 87.9% (71.8-96.6%). Mean radiologist sensitivity was 92.4% (86.9-97.9%) and specificity was 91.9% (83.4-100%). The model's overall accuracy was 88.2% (78.1-94.8%) which was comparable to the individual radiologist performance (92.2%, 90.1-94.2%) and consensus performance (95.6%, 87.6-99.1%).Conclusions: An automated machine-learning algorithm may accurately detect NAFLD on ultrasound.

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Background: Readability metrics provide us with an objective and efficient way to assess the quality of educational texts. We can use the readability measures for finding assessment items that are difficult to read for a given grade level. Hard-to-read math word problems can put some students at a disadvantage if they are behind in their literacy learning. Despite their math abilities, these students can perform poorly on difficult-to-read word problems because of their poor reading skills. Less readable math tests can create equity issues for students who are relatively new to the language of assessment. Less readable test items can also affect the assessment's construct validity by partially measuring reading comprehension.Objectives: This study shows how large language models help us improve the readability of math assessment items. Methods:We analysed 250 test items from grades 3 to 5 of EngageNY, an opensource curriculum. We used the GPT-3 AI system to simplify the text of these math word problems. We used text prompts and the few-shot learning method for the simplification task.Results and Conclusions: On average, GPT-3 AI produced output passages that showed improvements in readability metrics, but the outputs had a large amount of noise and were often unrelated to the input. We used thresholds over text similarity metrics and changes in readability measures to filter out the noise. We found meaningful simplifications that can be given to item authors as suggestions for improvement.Takeaways: GPT-3 AI is capable of simplifying hard-to-read math word problems.The model generates noisy simplifications using text prompts or few-shot learning methods. The noise can be filtered using text similarity and readability measures. The meaningful simplifications AI produces are sound but not ready to be used as a direct replacement for the original items. To improve test quality, simplifications can be suggested to item authors at the time of digital question authoring.