ArticleComputational intelligence and neuroscience2022
An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Lyme borreliosis diagnosis: state of the art of improvements and innovations.BMC microbiology · 2023Review
- Retracted: An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease.Computational intelligence and neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-07-26Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similarities with other diseases such as drug rash. Given the potentially serious consequences of unnecessary antimicrobial treatments, it is essential to understand frequent and uncommon diagnoses that explain symptoms in this population. Recently, deep learning models have been used for the diagnosis of various rash-related diseases. However, these models suffer from overfitting and color variation problems. To overcome these problems, an efficient stacked deep transfer learning model is proposed that can efficiently distinguish between patients infected with Lyme (+) or infected with other infections. 2
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.