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Mashup Score: 12
Rupa Sarkar, Editor-in-Chief and Diana Samuel, Senior Editor at The Lancet Digital Health, in conversation with the journal’s authors, explore their latest research and its impact on people’s health, healthcare, and health policy. A monthly audio companion to this open access journal, this podcast covers a broad range of topics, from using machine learning to predict mortality in prostate cancer and the need for feminist intersectionality in digital health, to how algorithms can predict a patient’s race
Source: www.thelancet.comCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 17FaceAge, a deep learning system to estimate biological age from face photographs to improve prognostication: a model development and validation study - 2 month(s) ago
Our results suggest that a deep learning model can estimate biological age from face photographs and thereby enhance survival prediction in patients with cancer. Further research, including validation in larger cohorts, is needed to verify these findings in patients with cancer and to establish whether the findings extend to patients with other diseases. Subject to further testing and validation, approaches such as FaceAge could be used to translate a patient’s visual appearance into objective, quantitative, and clinically valuable measures.
Source: www.thelancet.comCategories: General Medicine News, Future of MedicineTweet-
NEW Research: FaceAge, a #DeepLearning system to estimate biological age from face photographs to improve prognostication: a model development and validation study. @HugoAerts @denbonte @Dr_RayMak Read it here: https://t.co/fAAWybFCG5 https://t.co/jub9Qoem1N
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Mashup Score: 0Deep Learning Model for Outcome Predictions in Oropharyngeal Cancer - 2 month(s) ago
This prognostic study tests a computed tomography–based deep learning model that integrates primary tumor and lymph node features to predict outcomes in p16+ oropharyngeal squamous cell carcinoma and to identify patients with stage I disease who may derive added benefit associated with chemotherapy.
Source: jamanetwork.comCategories: General Medicine NewsTweet-
A CT-based #DeepLearning classifier was developed and validated to integrate primary tumor and lymph node features, predicting outcomes in p16+ OPSCC and identifying stage I patients who may benefit from added chemotherapy. https://t.co/ogwr3BJmKP @skhalighi
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Mashup Score: 1Artificial Intelligence Is Brittle: We Need to Do Better | Radiology: Artificial Intelligence - 3 month(s) ago
Can’t sign in? Forgot your password? If the address matches an existing account you wil l receive an email with instructions to reset your password. Can’t sign in? Forgot your username? 1. Hsu W, Hippe DS, Nakhaei N, et al.. JAMA Netw Open 2022;5(11):e2242343. 2. Voter AF, Larson ME, Garrett JW, Yu JJ.. AJNR Am J Neuroradiol 2021;42(8):1550–1556. 3. Johnson DC, Raman SS, Mirak SA, et al.. Eur Urol 2019;75(5):712–720. 4. Rouvière O, Souchon R, Lartizien C, et al.. BMJ Open 2022;12(2):e051274. 5. de
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet-
RT @Radiology_AI: #DeepLearning to predict prostate cancer aggressiveness https://t.co/FT7cn8vXbk @abhisuri97 @radiologyUCLA @bioengUCLA #P…
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Mashup Score: 17AI-Designed Antivenom Counteracts Toxins from Snakebites in Mice - 6 month(s) ago
The AI-designed proteins neutralized lethal 3FTx toxins found in snake venom, which are often the reason that traditional antivenoms fail.
Source: www.genengnews.comCategories: General Medicine News, Future of MedicineTweet-
AI-Designed Antivenom Counteracts Toxins from Snakebites in Mice Scientists led by David Baker, PhD and Timothy Patrick Jenkins, PhD, have used #deeplearning tools to design proteins that bind to and neutralize toxins found in the venom of deadly cobras. https://t.co/bHNAs5T99A
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Mashup Score: 16AI-Designed Antivenom Counteracts Toxins from Snakebites in Mice - 6 month(s) ago
The AI-designed proteins neutralized lethal 3FTx toxins found in snake venom, which are often the reason that traditional antivenoms fail.
Source: www.genengnews.comCategories: General Medicine News, Future of MedicineTweet-
AI-Designed Antivenom Counteracts Toxins from Snakebites in Mice Scientists led by David Baker, PhD and Timothy Patrick Jenkins, PhD, have used #deeplearning tools to design proteins that bind to and neutralize toxins found in the venom of deadly cobras. https://t.co/bHNAs5T99A
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Mashup Score: 4A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy - 6 month(s) ago
The low false positive rate, the low false negative rate, and the high accuracy in flagging errors show that DL-SpiQA is an effective, AI-driven, automated quality assurance tool that could be used to identify anatomic spine variants and errors in targeting at the anatomic level. The tool could therefore help improve the safety of spine radiotherapy. Further external validation and tailoring is needed.
Source: www.thelancet.comCategories: General Medicine News, Future of MedicineTweet-
NEW Research: A prospectively deployed #DeepLearning-enabled automated quality assurance tool for oncological palliative spine radiation therapy. @Dr_RayMak Read it here: https://t.co/QDf1PCPk69 https://t.co/nST2jWlXqL
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Mashup Score: 2AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study | Radiology: Artificial Intelligence - 8 month(s) ago
Mammography screening supported by deep learning–based artificial intelligence (AI) solutions can potentially reduce workload without compromising breast cancer detection accuracy, but the site of deployment in the workflow might be crucial. This retrospective study compared three simulated AI-integrated screening scenarios with standard double reading with arbitration in a sample of 249 402 mammograms from a representative screening population. A commercial AI system replaced the first reader (scenario 1: integrated AIfirst), the second reader (scenario 2: integrated AIsecond), or both readers for triaging of low- and high-risk cases (scenario 3: integrated AItriage). AI threshold values were chosen based partly on previous validation and setting the screen-read volume reduction at approximately 50% across scenarios. Detection accuracy measures were calculated. Compared with standard double reading, integrated AIfirst showed no evidence of a difference in accuracy metrics except for a
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet-
RT @Radiology_AI: Can #DeepLearning substitute for a 2nd-reader radiologist? https://t.co/1ndup1a5NJ @OUHhospital @UniSouthDenmark @uni_cop…
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Mashup Score: 1Digital profiling of gene expression from histology images with linearized attention - 8 month(s) ago
Nature Communications – Predicting gene alterations and expression from whole-slide images (WSIs) can be a cost-efficient solution for cancer profiling. Here, the authors develop SEQUOIA, a…
Source: www.nature.comCategories: General Medicine News, Hem/OncsTweet-
SCI member @ogevaert & others show how transformer-based #DeepLearning models, combining algorithmic advances with biological analysis, can predict high-dimensional gene expression from whole-slide histology images. https://t.co/chk7H57dnX https://t.co/RtpEjniVk0
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Mashup Score: 4Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge - 9 month(s) ago
Deep learning has shown great potential to automate abdominal organ segmentation and quantification. However, most existing algorithms rely on expert annotations and do not have comprehensive evaluations in real-world multinational settings. To address these limitations, we organised the FLARE 2022 challenge to benchmark fast, low-resource, and accurate abdominal organ segmentation algorithms. We first constructed an intercontinental abdomen CT dataset from more than 50 clinical research groups.
Source: www.thelancet.comCategories: General Medicine News, Future of MedicineTweet-
NEW Resource: "Unleashing the strengths of unlabelled data in #DeepLearning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge". @JunMa_11 @BoWang87 Read it here: https://t.co/P51O9kB0aS https://t.co/ZcKyuwLnso
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📢 NEW podcast: @HugoAerts and @Dr_RayMak join us to discuss their group's paper on FageAge: a #DeepLearning system to estimate biological age from face photographs. @MassGenBrigham Listen here: https://t.co/eYtUVwHwqv Read the paper here: https://t.co/fAAWybFCG5