1
|
Lella KK, Jagadeesh MS, Alphonse PJA. Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN. Health Inf Sci Syst 2024; 12:22. [PMID: 38469455 PMCID: PMC10924857 DOI: 10.1007/s13755-024-00283-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 02/21/2024] [Indexed: 03/13/2024] Open
Abstract
The utilization of lung sounds to diagnose lung diseases using respiratory sound features has significantly increased in the past few years. The Digital Stethoscope data has been examined extensively by medical researchers and technical scientists to diagnose the symptoms of respiratory diseases. Artificial intelligence-based approaches are applied in the real universe to distinguish respiratory disease signs from human pulmonary auscultation sounds. The Deep CNN model is implemented with combined multi-feature channels (Modified MFCC, Log Mel, and Soft Mel) to obtain the sound parameters from lung-based Digital Stethoscope data. The model analysis is observed with max-pooling and without max-pool operations using multi-feature channels on respiratory digital stethoscope data. In addition, COVID-19 sound data and enriched data, which are recently acquired data to enhance model performance using a combination of L2 regularization to overcome the risk of overfitting because of less respiratory sound data, are included in the work. The suggested DCNN with Max-Pooling on the improved dataset demonstrates cutting-edge performance employing a multi-feature channels spectrogram. The model has been developed with different convolutional filter sizes (1 × 12 , 1 × 24 , 1 × 36 , 1 × 48 , and 1 × 60 ) that helped to test the proposed neural network. According to the experimental findings, the suggested DCNN architecture with a max-pooling function performs better to identify respiratory disease symptoms than DCNN without max-pooling. In order to demonstrate the model's effectiveness in categorization, it is trained and tested with the DCNN model that extract several modalities of respiratory sound data.
Collapse
Affiliation(s)
- Kranthi Kumar Lella
- School of Computer Science and Engineering, VIT-AP University, Vijayawada, Guntur, Andhra Pradesh 522237 India
| | - M. S. Jagadeesh
- School of Computer Science and Engineering, VIT-AP University, Vijayawada, Guntur, Andhra Pradesh 522237 India
| | - P. J. A. Alphonse
- Department of Computer Applications, NIT Tiruchirappalli, Tiruchirappalli, Guntur, Tamil Nadu 620015 India
| |
Collapse
|
2
|
Rashidi HH, Ikram A, Dang LT, Bashir A, Zohra T, Ali A, Tanvir H, Mudassar M, Ravindran R, Akhtar N, Sikandar RI, Umer M, Akhter N, Butt R, Fennell BD, Khan IH. Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings. Sci Rep 2024; 14:14892. [PMID: 38937503 PMCID: PMC11211475 DOI: 10.1038/s41598-024-63707-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/31/2024] [Indexed: 06/29/2024] Open
Abstract
Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases.
Collapse
Affiliation(s)
- Hooman H Rashidi
- Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh Medical Center, and University of Pittsburgh School of Medicine, Pittsburgh, USA.
| | - Aamer Ikram
- National Institutes of Health, Islamabad, Pakistan
| | - Luke T Dang
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA
| | - Adnan Bashir
- Health Information Systems Program (HISP), Islamabad, Pakistan
| | | | - Amna Ali
- National Institutes of Health, Islamabad, Pakistan
| | - Hamza Tanvir
- National Institutes of Health, Islamabad, Pakistan
| | | | - Resmi Ravindran
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA
| | - Nasim Akhtar
- Pakistan Institute of Medical Sciences, Islamabad, Pakistan
| | | | - Mohammed Umer
- Rawalpindi Medical University-Rawalpindi, Rawalpindi, Pakistan
| | - Naeem Akhter
- Rawalpindi Medical University-Rawalpindi, Rawalpindi, Pakistan
| | - Rafi Butt
- Isolation Hospital and Infectious Treatment Centre, Islamabad, Pakistan
| | - Brandon D Fennell
- Department of Medicine, University of California, San Francisco, USA
| | - Imran H Khan
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA.
| |
Collapse
|
3
|
Dos Santos AL, Pinhati C, Perdigão J, Galante S, Silva L, Veloso I, Simões E Silva AC, Oliveira EA. Machine learning algorithms to predict outcomes in children and adolescents with COVID-19: A systematic review. Artif Intell Med 2024; 150:102824. [PMID: 38553164 DOI: 10.1016/j.artmed.2024.102824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/10/2023] [Accepted: 02/21/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND AND OBJECTIVES We aimed to analyze the study designs, modeling approaches, and performance evaluation metrics in studies using machine learning techniques to develop clinical prediction models for children and adolescents with COVID-19. METHODS We searched four databases for articles published between 01/01/2020 and 10/25/2023, describing the development of multivariable prediction models using any machine learning technique for predicting several outcomes in children and adolescents who had COVID-19. RESULTS We included ten articles, six (60 % [95 % confidence interval (CI) 0.31 - 0.83]) were predictive diagnostic models and four (40% [95 % CI 0.170.69]) were prognostic models. All models were developed to predict a binary outcome (n= 10/10, 100 % [95 % CI 0.72-1]). The most frequently predicted outcome was disease detection (n=3/10, 30% [95 % CI 0.11-0.60]). The most commonly used machine learning models in the studies were tree-based (n=12/33, 36.3% [95 % CI 0.17-0.47]) and neural networks (n=9/27, 33.2% [95% CI 0.15-0.44]). CONCLUSION Our review revealed that attention is required to address problems including small sample sizes, inconsistent reporting practices on data preparation, biases in data sources, lack of reporting metrics such as calibration and discrimination, hyperparameters and other aspects that allow reproducibility by other researchers and might improve the methodology.
Collapse
Affiliation(s)
- Adriano Lages Dos Santos
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil; Federal Institute of Education, Science and Technology of Minas Gerais (IFMG), Belo Horizonte, Brazil.
| | - Clara Pinhati
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Jonathan Perdigão
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Stella Galante
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Ludmilla Silva
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Isadora Veloso
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Ana Cristina Simões E Silva
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Eduardo Araújo Oliveira
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| |
Collapse
|
4
|
Introcaso G, Galotta A, Salvini L, Faioni EM, Bonomi A, Assanelli E, Biondi ML. Leukocyte cell population data as potential markers of COVID-19 disease characterization. J Med Biochem 2023; 42:454-459. [PMID: 37790201 PMCID: PMC10543122 DOI: 10.5937/jomb0-41589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/07/2023] [Indexed: 10/05/2023] Open
Abstract
Background The usefulness of leukocyte cell population data (CPD) is currently being investigated. In COVID-19 pandemic several reports showed the clinical importance of hematological parameters. Our study aimed to assess CPDs in Sars CoV-2 patients as new disease markers. Methods From February to April 2020 (1st wave) 540 and from September to December 2020 (2nd wave) 2821 patients respectively were enrolled. SARS CoV-2 infection diagnosis was carried out by Multiplex rRT-PCR from nasopharyngeal swabs. CPDs were detected by XN 2000 hematology analyzer (Sysmex Corporation). A comparison between two disease waves was performed. Additionally, C-reactive protein (CRP) and lactate dehydrogenase (LDH) were assayed. Results CPDs were classified into: cell complextity, DNA/RNA content and abnormal sized cells. We detected parameters increased from the reference population for all cell types for both 1st and 2nd wave (p<0.05). However, in the 2nd vs 1st wave 5 CPDs vs 9 CPDs were found. In addition we observed higher CPD values of the 1st compared to 2nd wave: (NE-SFL) (p<0.001), (LY-Y) (p<0.0001), (LY-Z) (p<0.0001), (MO-X) (p<0.0001), (MO-Y) (p<0.0001). These findings were confirmed by the higher concentrations of CRP and LDH in the 1st vs 2nd wave: 17.3 mg/L (8.5-59.3) vs 6.3 mg/L (2.3-17.6) (p<0.001) and 241.5 IU/L (201-345) vs 195 IU/L (174-228) (p< 0.001) (median, interquartile range) respectively. Conclusions CPDs showed increased cell activation in 1st wave patients confirmed by clinical and biochemical data, associated with worse clinical conditions. Results highlighted the CPDs as disease characterization markers or useful for a risk model.
Collapse
Affiliation(s)
- Giovanni Introcaso
- Università di Milano, Centro Cardiologico Monzino IRCCS, Unit of Laboratory Medicine, Milan, Italy
| | - Arianna Galotta
- Centro Cardiologico Monzino IRCCS, Unit of Biostatistics, Milan, Italy
| | - Laura Salvini
- Centro Cardiologico Monzino IRCCS, Emergency Department, Milan, Italy
| | | | - Alice Bonomi
- Centro Cardiologico Monzino IRCCS, Unit of Biostatistics, Milan, Italy
| | - Emilio Assanelli
- Centro Cardiologico Monzino IRCCS, Emergency Department, Milan, Italy
| | - Maria Luisa Biondi
- Università di Milano, Centro Cardiologico Monzino IRCCS, Unit of Laboratory Medicine, Milan, Italy
| |
Collapse
|
5
|
Bayraktar M, Tekin E, Kocak MN. How to diagnose COVID-19 in family practice? Usability of complete blood count as a COVID-19 diagnostic tool: a cross-sectional study in Turkey. BMJ Open 2023; 13:e069493. [PMID: 37068894 PMCID: PMC10111184 DOI: 10.1136/bmjopen-2022-069493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/19/2023] Open
Abstract
OBJECTIVE COVID-19 is currently diagnosed in hospital settings. An easy and practical diagnosis of COVID-19 is needed in primary care. For this purpose, the usability of complete blood count in the diagnosis of COVID-19 was investigated. DESIGN Retrospective, cross-sectional study. SETTING Single-centre study in a tertiary university hospital in Erzurum, Turkey. PARTICIPANTS Between March 2020 and February 2021, patients aged 18-70 years who applied to the hospital and underwent both complete blood count and reverse-transcription-PCR tests for COVID-19 were included and compared. Conditions affecting the test parameters (oncological-haematological conditions, chronic diseases, drug usage) were excluded. OUTCOME MEASURE The complete blood count and COVID-19 results of eligible patients identified using diagnostic codes [U07.3 (COVID-19) or Z03.8 (observation for other suspected diseases and conditions)] were investigated. RESULTS Of the 978 patients included, 39.4% (n=385) were positive for COVID-19 and 60.6% (n=593) were negative. The mean age was 41.5±14.5 years, and 53.9% (n=527) were male. COVID-19-positive patients were found to have significantly lower leucocyte, neutrophil, lymphocyte, monocyte, basophil, platelet and immature granulocyte (IG) values (p<0.001). Neutrophil/lymphocyte, neutrophil/monocyte and IG/lymphocyte ratios were also found to be significantly decreased (p<0.001). With logistic regression analysis, low lymphocyte count (OR 0.695; 95% CI 0.597 to 0.809) and low red cell distribution width-coefficient of variation (RDW-CV) (OR 0.887; 95% CI 0.818 to 0.962) were significantly associated with COVID-19 positivity. In receiver operating characteristic analysis, the cut-off values of lymphocyte and RDW-CV were 0.745 and 12.35, respectively. CONCLUSION Although our study was designed retrospectively and reflects regional data, it is important to determine that low lymphocyte count and RDW-CV can be used in the diagnosis of COVID-19 in primary care.
Collapse
Affiliation(s)
| | - Erdal Tekin
- Emergency Medicine, Ataturk University, Erzurum, Turkey
| | | |
Collapse
|
6
|
Lennerz JK, Salgado R, Kim GE, Sirintrapun SJ, Thierauf JC, Singh A, Indave I, Bard A, Weissinger SE, Heher YK, de Baca ME, Cree IA, Bennett S, Carobene A, Ozben T, Ritterhouse LL. Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML. Clin Chem Lab Med 2023; 61:544-557. [PMID: 36696602 DOI: 10.1515/cclm-2022-1151] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/13/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing. METHODS A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. RESULTS The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. CONCLUSIONS A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.
Collapse
Affiliation(s)
- Jochen K Lennerz
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
- Division of Research, Peter Mac Callum Cancer Centre, Melbourne, Australia
| | - Grace E Kim
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | | | - Julia C Thierauf
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
- Department of Otorhinolaryngology, Head and Neck Surgery, German Cancer Research Center (DKFZ), Heidelberg University Hospital and Research Group Molecular Mechanisms of Head and Neck Tumors, Heidelberg, Germany
| | - Ankit Singh
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Iciar Indave
- European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), Lisbon, Portugal
| | - Adam Bard
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Yael K Heher
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Ian A Cree
- International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France
| | - Shannon Bennett
- Department of Laboratory Medicine and Pathology (DLMP), Mayo Clinic, Rochester, MN, USA
| | - Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Tomris Ozben
- Medical Faculty, Dept. of Clinical Biochemistry, Akdeniz University, Antalya, Türkiye
- Medical Faculty, Clinical and Experimental Medicine, Ph.D. Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Lauren L Ritterhouse
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| |
Collapse
|
7
|
Carobene A, Cabitza F, Bernardini S, Gopalan R, Lennerz JK, Weir C, Cadamuro J. Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories. Clin Chem Lab Med 2023; 61:535-543. [PMID: 36327445 DOI: 10.1515/cclm-2022-1030] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVES The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, and there is little attention given to AI integration into laboratory medicine. METHODS We dedicated a session at the 3rd annual European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) strategic conference in 2022 to the topic of AI in the laboratory of the future. The speakers collaborated on generating a concise summary of the content that is presented in this paper. RESULTS The five key messages are (1) Laboratory specialists and technicians will continue to improve the analytical portfolio, diagnostic quality and laboratory turnaround times; (2) The modularized nature of laboratory processes is amenable to AI solutions; (3) Laboratory sub-specialization continues and from test selection to interpretation, tasks increase in complexity; (4) Expertise in AI implementation and partnerships with industry will emerge as a professional competency and require novel educational strategies for broad implementation; and (5) regulatory frameworks and guidances have to be adopted to new computational paradigms. CONCLUSIONS In summary, the speakers opine that the ability to convert the value-proposition of AI in the laboratory will rely heavily on hands-on expertise and well designed quality improvement initiative from within laboratory for improved patient care.
Collapse
Affiliation(s)
- Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federico Cabitza
- IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milan, Italy.,DISCo, Università Degli Studi di Milano-Bicocca, Milan, Italy
| | - Sergio Bernardini
- Unit of Laboratory Medicine, Tor Vergata University Hospital, Rome, Italy.,Department of Experimental Medicine, University of Tor Vergata, Rome, Italy
| | - Raj Gopalan
- Siemens Healthcare Diagnostics, Siemens Healthineers, Malvern, PA, USA
| | - Jochen K Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | | | - Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
| |
Collapse
|
8
|
Cardozo G, Tirloni SF, Pereira Moro AR, Marques JLB. Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review. JMIR BIOINFORMATICS AND BIOTECHNOLOGY 2022; 3:e40473. [PMID: 36644762 PMCID: PMC9828303 DOI: 10.2196/40473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/28/2022] [Accepted: 10/31/2022] [Indexed: 11/05/2022]
Abstract
Background In recent decades, the use of artificial intelligence has been widely explored in health care. Similarly, the amount of data generated in the most varied medical processes has practically doubled every year, requiring new methods of analysis and treatment of these data. Mainly aimed at aiding in the diagnosis and prevention of diseases, this precision medicine has shown great potential in different medical disciplines. Laboratory tests, for example, almost always present their results separately as individual values. However, physicians need to analyze a set of results to propose a supposed diagnosis, which leads us to think that sets of laboratory tests may contain more information than those presented separately for each result. In this way, the processes of medical laboratories can be strongly affected by these techniques. Objective In this sense, we sought to identify scientific research that used laboratory tests and machine learning techniques to predict hidden information and diagnose diseases. Methods The methodology adopted used the population, intervention, comparison, and outcomes principle, searching the main engineering and health sciences databases. The search terms were defined based on the list of terms used in the Medical Subject Heading database. Data from this study were presented descriptively and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; 2020) statement flow diagram and the National Institutes of Health tool for quality assessment of articles. During the analysis, the inclusion and exclusion criteria were independently applied by 2 authors, with a third author being consulted in cases of disagreement. Results Following the defined requirements, 40 studies presenting good quality in the analysis process were selected and evaluated. We found that, in recent years, there has been a significant increase in the number of works that have used this methodology, mainly because of COVID-19. In general, the studies used machine learning classification models to predict new information, and the most used parameters were data from routine laboratory tests such as the complete blood count. Conclusions Finally, we conclude that laboratory tests, together with machine learning techniques, can predict new tests, thus helping the search for new diagnoses. This process has proved to be advantageous and innovative for medical laboratories. It is making it possible to discover hidden information and propose additional tests, reducing the number of false negatives and helping in the early discovery of unknown diseases.
Collapse
Affiliation(s)
- Glauco Cardozo
- Federal Institute of Santa Catarina Florianópolis Brazil
| | | | | | | |
Collapse
|
9
|
Bellini C, Padoan A, Carobene A, Guerranti R. A survey on Artificial Intelligence and Big Data utilisation in Italian clinical laboratories. Clin Chem Lab Med 2022; 60:2017-2026. [PMID: 36067004 DOI: 10.1515/cclm-2022-0680] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/25/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVES The Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC) Big Data and Artificial Intelligence (BAI) Working Group promoted a survey to frame the knowledge, skills and technological predisposition in clinical laboratories. METHODS A questionnaire, focussing on digitization, information technology (IT) infrastructures, data accessibility, and BAI projects underway was sent to 1,351 SIBioC participants. The responses were evaluated using SurveyMonkey software and Google Sheets. RESULTS The 227 respondents (17%) from all over Italy (47% of 484 labs), mainly biologists, laboratory physicians and managers, mostly from laboratories of public hospitals, revealed lack of hardware, software and corporate Wi-Fi, and dearth of PCs. Only 25% work daily on clouds, while 65%-including Laboratory Directors-cannot acquire health data from sources other than laboratories. Only 50% of those with access can review a clinical patient's health record, while the other access only to laboratory information. The integration of laboratory data with other health data is mostly incomplete, which limits BAI-type analysis. Many are unaware of integration platforms. Over 90% report pulling data from the Laboratory Information System, with varying degrees of autonomy. Very few have already undertaken BAI projects, frequently relying on IT partnerships. The majority consider BAI as crucial in helping professional judgements, indicating a growing interest. CONCLUSIONS The questionnaire received relevant feedback from SIBioC participants. It highlighted the level of expertise and interest in BAI applications. None of the obstacles stands out more than the others, emphasising the need to all-around work: IT infrastructures, data warehouses, BAI analysis software acquisition, data accessibility and training.
Collapse
Affiliation(s)
- Claudia Bellini
- Clinical Chemistry Laboratory Analysis Unit, M isericordia Hospital Grosseto, South East Tuscany USL, Grosseto, Italy
| | - Andrea Padoan
- Department of Medicine-DIMED, University of Padova, Padova, Italy.,Department of Laboratory Medicine, University-Hospital of Padova, Padova, Italy
| | - Anna Carobene
- Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Roberto Guerranti
- Department of Medical Biotechnologies, University of Siena, Siena, Italy.,Clinical Pathology Unit, Innovation, Experimentation and Clinical and Translational Research Department, University Hospital of Siena, Siena, Italy
| |
Collapse
|
10
|
Carobene A, Milella F, Famiglini L, Cabitza F. How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data. Clin Chem Lab Med 2022; 60:1887-1901. [PMID: 35508417 DOI: 10.1515/cclm-2022-0182] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/22/2022] [Indexed: 12/13/2022]
Abstract
The current gold standard for COVID-19 diagnosis, the rRT-PCR test, is hampered by long turnaround times, probable reagent shortages, high false-negative rates and high prices. As a result, machine learning (ML) methods have recently piqued interest, particularly when applied to digital imagery (X-rays and CT scans). In this review, the literature on ML-based diagnostic and prognostic studies grounded on hematochemical parameters has been considered. By doing so, a gap in the current literature was addressed concerning the application of machine learning to laboratory medicine. Sixty-eight articles have been included that were extracted from the Scopus and PubMed indexes. These studies were marked by a great deal of heterogeneity in terms of the examined laboratory test and clinical parameters, sample size, reference populations, ML algorithms, and validation approaches. The majority of research was found to be hampered by reporting and replicability issues: only four of the surveyed studies provided complete information on analytic procedures (units of measure, analyzing equipment), while 29 provided no information at all. Only 16 studies included independent external validation. In light of these findings, we discuss the importance of closer collaboration between data scientists and medical laboratory professionals in order to correctly characterise the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the proper interpretation of the results, and gain actual utility by using machine learning methods in clinical practice.
Collapse
Affiliation(s)
- Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | | | - Federico Cabitza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,DISCo, Università Degli Studi di Milano-Bicocca, Milan, Italy
| |
Collapse
|
11
|
Pennestrì F, Banfi G. Artificial intelligence in laboratory medicine: fundamental ethical issues and normative key-points. Clin Chem Lab Med 2022; 60:1867-1874. [PMID: 35413163 DOI: 10.1515/cclm-2022-0096] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/18/2022] [Indexed: 12/15/2022]
Abstract
The contribution of laboratory medicine in delivering value-based care depends on active cooperation and trust between pathologist and clinician. The effectiveness of medicine more in general depends in turn on active cooperation and trust between clinician and patient. From the second half of the 20th century, the art of medicine is challenged by the spread of artificial intelligence (AI) technologies, recently showing comparable performances to flesh-and-bone doctors in some diagnostic specialties. Being the principle source of data in medicine, the laboratory is a natural ground where AI technologies can disclose the best of their potential. In order to maximize the expected outcomes and minimize risks, it is crucial to define ethical requirements for data collection and interpretation by-design, clarify whether they are enhanced or challenged by specific uses of AI technologies, and preserve these data under rigorous but feasible norms. From 2018 onwards, the European Commission (EC) is making efforts to lay the foundations of sustainable AI development among European countries and partners, both from a cultural and a normative perspective. Alongside with the work of the EC, the United Kingdom provided worthy-considering complementary advice in order to put science and technology at the service of patients and doctors. In this paper we discuss the main ethical challenges associated with the use of AI technologies in pathology and laboratory medicine, and summarize the most pertaining key-points from the guidelines and frameworks before-mentioned.
Collapse
Affiliation(s)
| | - Giuseppe Banfi
- IRCCS Istituto Ortopedico Galeazzi, Milan, Lombardia, Italy.,Università Vita-Salute San Raffaele, Milan, Italy
| |
Collapse
|
12
|
Cardozo G, Pintarelli GB, Andreis GR, Lopes ACW, Marques JLB. Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8114049. [PMID: 35392258 PMCID: PMC8983182 DOI: 10.1155/2022/8114049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 02/18/2022] [Accepted: 03/10/2022] [Indexed: 12/28/2022]
Abstract
Most patients with diabetes mellitus are asymptomatic, which leads to delayed and more complex treatment. At the same time, most individuals are routinely subjected to standard clinical laboratory examinations, which create large health datasets over a lifetime. Computer processing has been used to search for health anomalies and predict diseases using clinical examinations. This work studied machine learning models to support the screening of diabetes through routine laboratory tests using data from laboratory tests of 62,496 patients. The classification and regression models used were the K-nearest neighbor, support vector machines, Bayes naïve, random forest models, and artificial neural networks. Glycated hemoglobin, a test used for diabetes diagnosis, was used as the target. Regression models calculated glycated hemoglobin directly and were later classified. The performance of classification computer models has been studied under various subdataset partitions and combinations (e.g., healthy, prediabetic, and diabetes, as well as no healthy and no diabetes). The best single performance was achieved with the artificial neural network model when detecting prediabetes or diabetes. The artificial neural network classification model scored 78.1%, 78.7%, and 78.4% for sensitivity, precision, and F1 scores, respectively, when identifying no healthy group. Other models also had good results, depending on what is desired. Machine learning-based models can predict glycated hemoglobin values from routine laboratory tests and can be used as a screening tool to refer a patient for further testing.
Collapse
Affiliation(s)
- Glauco Cardozo
- Academic Department of Health and Services, Federal Institute of Santa Catarina, Florianopolis, SC 88020-300, Brazil
- Institute of Biomedical Engineering, Federal University of Santa Catarina, Florianopolis, SC 88040-900, Brazil
| | - Guilherme Brasil Pintarelli
- Institute of Biomedical Engineering, Federal University of Santa Catarina, Florianopolis, SC 88040-900, Brazil
| | - Guilherme Rettore Andreis
- Institute of Biomedical Engineering, Federal University of Santa Catarina, Florianopolis, SC 88040-900, Brazil
| | | | - Jefferson Luiz Brum Marques
- Institute of Biomedical Engineering, Federal University of Santa Catarina, Florianopolis, SC 88040-900, Brazil
| |
Collapse
|