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Machine learning for medical imaging: methodological failures and recommendations for the future.

ABSTRACT: Research in computer analysis of medical images bears many promises to improve patients' health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives, such as optimizing for publication. In this paper we review roadblocks to developing and assessing methods. Building our analysis on evidence from the literature and data challenges, we show that at every step, potential biases can creep in. On a positive note, we also discuss on-going efforts to counteract these problems. Finally we provide recommendations on how to further address these problems in the future.

SUBMITTER: Varoquaux G

PROVIDER: S-EPMC9005663 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Machine learning for medical imaging: methodological failures and recommendations for the future.

Varoquaux Gaël G Cheplygina Veronika V

NPJ digital medicine 20220412 1

Research in computer analysis of medical images bears many promises to improve patients' health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives, such as optimizing for publication. In this paper we review roadblocks to developing and assessing methods. Building our analysis on evidence from the literature and data challenges, we show that at every step, potential biases can creep in. On a ...[more]

PMID: 35413988

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Project description:BackgroundMultiple clinical trials failed to demonstrate the efficacy of hydrocortisone, ascorbic acid, and thiamine (HAT) in sepsis. These trials were dominated by patients with pulmonary sepsis and have not accounted for differences in the inflammatory responses across varying etiologies of injury/illness. Hydrocortisone, ascorbic acid, and thiamine have previously revealed tremendous benefits in animal peritonitis sepsis models (cecal ligation and puncture [CLP]) in contradiction to the various clinical trials. The impact of HAT remains unclear in pulmonary sepsis. Our objective was to investigate the impact of HAT in pneumonia, consistent with the predominate etiology in the discordant clinical trials. We hypothesized that, in a pulmonary sepsis model, HAT would act synergistically to reduce end-organ dysfunction by the altering the inflammatory response, in a unique manner compared with CLP.MethodsUsing Pseudomonas aeruginosa pneumonia, a pulmonary sepsis model (pneumonia [PNA]) was compared directly to previously investigated intra-abdominal sepsis models. Machine learning applied to early vital signs stratified animals into those predicted to die (pDie) versus predicted to live (pLive). Animals were then randomized to receive antibiotics and fluids (vehicle [VEH]) vs. HAT). Vitals, cytokines, vitamin C, and markers of liver and kidney function were assessed in the blood, bronchoalveolar lavage, and organ homogenates.ResultsPNA was induced in 119 outbred wild-type Institute of Cancer Research mice (predicted mortality approximately 50%) similar to CLP. In PNA, interleukin 1 receptor antagonist in 72-hour bronchoalveolar lavage was lower with HAT (2.36 ng/mL) compared with VEH (4.88 ng/mL; p = 0.04). The remaining inflammatory cytokines and markers of liver/renal function showed no significant difference with HAT in PNA. PNA vitamin C levels were 0.62 mg/dL (pDie HAT), lower than vitamin C levels after CLP (1.195 mg/dL). Unlike CLP, PNA mice did not develop acute kidney injury (blood urea nitrogen: pDie, 33.5 mg/dL vs. pLive, 27.6 mg/dL; p = 0.17). Furthermore, following PNA, HAT did not significantly reduce microscopic renal oxidative stress (mean gray area: pDie, 16.64 vs. pLive, 6.88; p = 0.93). Unlike CLP where HAT demonstrated a survival benefit, HAT had no impact on survival in PNA.ConclusionHydrocortisone, ascorbic acid, and thiamine therapy has minimal benefits in pneumonia. The inflammatory response induced by pulmonary sepsis is unique compared with the response during intra-abdominal sepsis. Consequently, different etiologies of sepsis respond differently to HAT therapy.

Dataset Information

Machine learning for medical imaging: methodological failures and recommendations for the future.

Publications

Machine learning for medical imaging: methodological failures and recommendations for the future.

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