Artificial intelligence in the practice of pulmonology: The future is now

Chauhan, NishantKumar; Asfahan, Shahir; Dutt, Naveen; Jalandra, Ramniwas

doi:10.4103/lungindia.lungindia_692_21

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Recently, AI-based applications and other mathematical techniques have been employed in research opportunities for different applications regarding lung characterization in several pathologies [21][22][23]: the novel severe acute respiratory syndrome-coronavirus-2 pandemic, asthma, and COPD; interpretation of pulmonary function tests, thoracic CT screening, patient's prognosis in lung cancer, and others in the practice of pulmonology medicine [24][25][26][27][28][29][30][31]. According to the current evidence, AI, machine learning, and engineering tools are mainly used in the management of respiratory diseases, complementary to human experts' decisions.…”

Section: Discussionmentioning

confidence: 99%

Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital

Stoichita,

Ghita,

Mahler

et al. 2023

JCM

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Background: In December 2019 the World Health Organization announced that the widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection had become a global pandemic. The most affected organ by the novel virus is the lung, and imaging exploration of the thorax using computer tomography (CT) scanning and X-ray has had an important impact. Materials and Methods: We assessed the prevalence of lung lesions in vaccinated versus unvaccinated SARS-CoV-2 patients using an artificial intelligence (AI) platform provided by Medicai. The software analyzes the CT scans, performing the lung and lesion segmentation using a variant of the U-net convolutional network. Results: We conducted a cohort study at a tertiary lung hospital in which we included 186 patients: 107 (57.52%) male and 59 (42.47%) females, of which 157 (84.40%) were not vaccinated for SARS-CoV-2. Over five times more unvaccinated patients than vaccinated ones are admitted to the hospital and require imaging investigations. More than twice as many unvaccinated patients have more than 75% of the lungs affected. Patients in the age group 30–39 have had the most lung lesions at almost 69% of both lungs affected. Compared to vaccinated patients with comorbidities, unvaccinated patients with comorbidities had developed increased lung lesions by 5%. Conclusion: The study revealed a higher percentage of lung lesions among unvaccinated SARS-CoV-2-positive patients admitted to The National Institute of Pulmonology “Marius Nasta” in Bucharest, Romania, underlining the importance of vaccination and also the usefulness of artificial intelligence in CT interpretation.

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Section: Discussionmentioning

confidence: 99%

Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital

Stoichita,

Ghita,

Mahler

et al. 2023

JCM

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“…28 In high-resolution computed tomography evaluation, AI provides a classification of fibrotic lung disease with an accuracy that matches the human level; in the recent SARS-CoV-2 pandemic, AI was used in coding CT scans of the lungs of COVID-19 patients. 29 The AI algorithm called DLAD (Deep Learning based Automatic Detection), created in 2018 by Seoul researchers to analyze chest X-rays, detected abnormal cell growth and outperformed 17 of 18 doctors. 30 In endocrinology, AI technologies have proven their usefulness in the diagnosis and management of various endocrinopathies.…”

Section: Applications Of Ai In Medicinementioning

confidence: 99%

Ethical Dilemmas of Using Artificial Intelligence in Medicine

Astărăstoae,

Rogozea,

Leaşu

et al. 2024

American Journal of Therapeutics

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Background: Artificial intelligence (AI) is considered the fourth industrial revolution that will change the evolution of humanity technically and relationally. Although the term has been around since 1956, it has only recently become apparent that AI can revolutionize technologies and has many applications in the medical field. Areas of Uncertainty: The ethical dilemmas posed by the use of AI in medicine revolve around issues related to informed consent, respect for confidentiality, protection of personal data, and last but not least the accuracy of the information it uses. Data Sources: A literature search was conducted through PubMed, MEDLINE, Plus, Scopus, and Web of Science (2015–2022) using combinations of keywords, including: AI, future in medicine, and machine learning plus ethical dilemma. Ethics and Therapeutic Advances: The ethical analysis of the issues raised by AI used in medicine must mainly address nonmaleficence and beneficence, both in correlation with patient safety risks, ability versus inability to detect correct information from inadequate or even incorrect information. The development of AI tools that can support medical practice can increase people's access to medical information, to obtain a second opinion, for example, but it is also a source of concern among health care professionals and especially bioethicists about how confidentiality is maintained and how to maintain cybersecurity. Another major risk may be related to the dehumanization of the medical act, given that, at least for now, empathy and compassion are accessible only to human beings. Conclusions: AI has not yet managed to overcome certain limits, lacking moral subjectivity, empathy, the level of critical thinking is still insufficient, but no matter who will practice preventive or curative medicine in the next period, they will not be able to ignore AI, which under human control can be an important tool in medical practice.

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“…While inhalation injuries are a uniquely burn-related complication, imaging techniques on bronchoscopy images are applied in other fields, such as pulmonology [ 26 , 27 ]. Machine learning has been utilized and shown to accurately classify fibrotic lung disease using CT imaging, diagnose and differentiate COVID-19-associated pneumonia from other pneumonia, and even accurately diagnose lung cancer using low-dose CT imaging [ 26 ]. One study took this use of machine learning even further.…”

Section: Introductionmentioning

confidence: 99%

Inhalation Injury Grading Using Transfer Learning Based on Bronchoscopy Images and Mechanical Ventilation Period

Pang

Zeitouni

et al. 2022

Sensors

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The abbreviated injury score (AIS) is commonly used as a grading system for inhalation injuries. While inhalation injury grades have inconsistently been shown to correlate positively with the time mechanical ventilation is needed, grading is subjective and relies heavily on the clinicians’ experience and expertise. Additionally, no correlation has been shown between these patients’ inhalation injury grades and outcomes. In this paper, we propose a novel inhalation injury grading method which uses deep learning algorithms in bronchoscopy images to determine the injury grade from the carbonaceous deposits, blistering, and fibrin casts in the bronchoscopy images. The proposed method adopts transfer learning and data augmentation concepts to enhance the accuracy performance to avoid overfitting. We tested our proposed model on the bronchoscopy images acquired from eighteen patients who had suffered inhalation injuries, with the degree of severity 1, 2, 3, 4, 5, or 6. As performance metrics, we consider accuracy, sensitivity, specificity, F-1 score, and precision. Experimental results show that our proposed method, with both transfer learning and data augmentation components, provides an overall 86.11% accuracy. Moreover, the experimental results also show that the performance of the proposed method outperforms the method without transfer learning or data augmentation.

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Artificial intelligence in the practice of pulmonology: The future is now

Cited by 4 publications

References 12 publications

Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital

Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital

Ethical Dilemmas of Using Artificial Intelligence in Medicine

Inhalation Injury Grading Using Transfer Learning Based on Bronchoscopy Images and Mechanical Ventilation Period

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