{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T15:34:51Z","timestamp":1784043291960,"version":"3.55.0"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:00:00Z","timestamp":1771545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Accurate diagnosis of periprosthetic joint infection (PJI) remains challenging, particularly in culture-negative and borderline cases where current practices lead to high diagnostic uncertainty. SynTuition\u2122, a machine-learning-based probability score integrating preoperative biomarkers, was developed to support clinical decision-making. This study compared its diagnostic performance and economic impact with standard physician practice. Methods: A total of 12 physicians provided diagnoses of 274 clinical vignettes representing suspected PJI cases. SynTuition probabilities were converted to binary diagnostic classifications using a validated threshold. Diagnostic accuracy, agreement, indecision rates, decision curve analysis, and misdiagnosis-related costs were evaluated. Results: SynTuition achieved an overall percent agreement of 96.0% when compared against the expert adjudicated clinical reference, outperforming the pooled physician group at 90.8%. Physicians showed high indecision (38\u201348%) in inconclusive 2018 ICM cases, whereas SynTuition generated a definitive diagnosis with an 86.7% agreement against expert adjudication. Decision curve analysis demonstrated a higher net benefit for SynTuition across a broad range of thresholds, reducing projected unnecessary revision by up to 5.8%. Economic modeling showed a reduction in misdiagnosis-related costs from $6.9 million to $2.9 million per 1000 suspected PJI cases, yielding estimated savings of $4000 per suspected case. Conclusions: SynTuition demonstrated high diagnostic accuracy, lower uncertainty, and significant clinical and economic advantages over routine physician practice, supporting its integration into clinical decision-making for suspected PJI, particularly in diagnostically ambiguous cases.<\/jats:p>","DOI":"10.3390\/diagnostics16040626","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T11:39:10Z","timestamp":1771846750000},"page":"626","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning-Driven Probability Scoring Enhances Diagnostic Certainty and Reduces Costs in Suspected Periprosthetic Joint Infection"],"prefix":"10.3390","volume":"16","author":[{"given":"Jim","family":"Parr","sequence":"first","affiliation":[{"name":"Data Science and Machine Learning, Zimmer Biomet, Swindon SN5 6NX, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Van","family":"Thai-Paquette","sequence":"additional","affiliation":[{"name":"Diagnostics Research and Development, Zimmer Biomet, Claymont, DE 19703, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Worden","sequence":"additional","affiliation":[{"name":"Health Economics, Zimmer Biomet, Centennial, CO 80015, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Baker","sequence":"additional","affiliation":[{"name":"University of Louisville Physicians\u2014Orthopedics, University of Louisville Health, Louisville, KY 40202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Edwards","sequence":"additional","affiliation":[{"name":"Bowen Hefley Orthopedics, Little Rock, AR 72207, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9683-8710","authenticated-orcid":false,"given":"Krista O\u2019Shaughnessey","family":"Toler","sequence":"additional","affiliation":[{"name":"Diagnostics Research and Development, Zimmer Biomet, Claymont, DE 19703, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fr\u00f6schen, F.S., Randau, T.M., Franz, A., Molitor, E., and Hischebeth, G.T.R. (2022). Microbiological Profiles of Patients with Periprosthetic Joint Infection of the Hip or Knee. Diagnostics, 12.","DOI":"10.3390\/diagnostics12071654"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2194","DOI":"10.1007\/s11999-017-5396-4","article-title":"Periprosthetic Joint Infection Is the Main Cause of Failure for Modern Knee Arthroplasty: An Analysis of 11,134 Knees","volume":"475","author":"Koh","year":"2017","journal-title":"Clin. Orthop. Relat. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"138205","DOI":"10.52965\/001c.138205","article-title":"Periprosthetic Joint Infection: A Multifaceted Burden Undermining Arthroplasty Success","volume":"17","author":"Aftab","year":"2025","journal-title":"Orthop. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3287","DOI":"10.1007\/s00167-020-06154-7","article-title":"The projected volume of primary and revision total knee arthroplasty will place an immense burden on future health care systems over the next 30 years","volume":"29","author":"Klug","year":"2021","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101","DOI":"10.5194\/jbji-10-101-2025","article-title":"Debridement, antimicrobial therapy, and implant retention (DAIR) as curative strategy for acute periprosthetic hip and knee infections: A position paper of the European Bone\u2009&\u2009Joint Infection Society (EBJIS)","volume":"10","author":"Sigmund","year":"2025","journal-title":"J. Bone Jt. Infect."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1016\/j.arth.2014.03.009","article-title":"Definition of periprosthetic joint infection","volume":"29","author":"Parvizi","year":"2014","journal-title":"J. Arthroplast."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.arth.2018.09.045","article-title":"Hip and knee section, what is the definition of a periprosthetic joint infection (PJI) of the knee and the hip? Can the same criteria be used for both joints?: Proceedings of International Consensus on Orthopedic Infections","volume":"34","author":"Shohat","year":"2019","journal-title":"J. Arthroplast."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1302\/0301-620X.103B1.BJJ-2020-1381.R1","article-title":"The EBJIS definition of periprosthetic joint infection","volume":"103-B","author":"McNally","year":"2021","journal-title":"Bone Jt. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/cid\/cis966","article-title":"Executive summary: Diagnosis and management of prosthetic joint infection: Clinical practice guidelines by the Infectious Diseases Society of America","volume":"56","author":"Osmon","year":"2013","journal-title":"Clin. Infect. Dis."},{"key":"ref_10","first-page":"e31418","article-title":"Physician use of multiple criteria to diagnose periprosthetic joint infection may be less accurate than the use of an individual test","volume":"14","author":"Deirmengian","year":"2022","journal-title":"Cureus"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1093\/cid\/ciad360","article-title":"Periprosthetic joint infection: Current clinical challenges","volume":"77","author":"Nelson","year":"2023","journal-title":"Clin. Infect. Dis."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rocchi, C., Di Maio, M., Bulgarelli, A., Chiappetta, K., La Camera, F., Grappiolo, G., and Loppini, M. (2025). Agreement Analysis Among Hip and Knee Periprosthetic Joint Infections Classifications. Diagnostics, 15.","DOI":"10.3390\/diagnostics15091172"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"S222","DOI":"10.1016\/j.arth.2025.08.041","article-title":"ICM 2025: New Technologies like Artificial Intelligence, Robotics, and Anti-Biofilm","volume":"41","author":"Grenho","year":"2025","journal-title":"J. Arthroplast."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s42836-023-00195-2","article-title":"Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: A systematic review","volume":"5","author":"Chong","year":"2023","journal-title":"Arthroplasty"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Di Matteo, V., Morandini, P., Savevski, V., Grappiolo, G., and Loppini, M. (2025). Preoperative Diagnosis of Periprosthetic Infection in Patients Undergoing Hip or Knee Revision Arthroplasties: Development and Validation of Machine Learning Algorithm. Diagnostics, 15.","DOI":"10.3390\/diagnostics15050539"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1055\/s-0041-1741396","article-title":"The Use of Artificial Neural Networks for the Prediction of Surgical Site Infection Following TKA","volume":"36","author":"Yeo","year":"2023","journal-title":"J. Knee Surg."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s00402-025-05757-4","article-title":"Machine learning to predict periprosthetic joint infections following primary total hip arthroplasty using a national database","volume":"145","author":"Salimy","year":"2025","journal-title":"Arch. Orthop. Trauma Surg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.arth.2021.09.005","article-title":"Periprosthetic Joint Infection Prediction via Machine Learning: Comprehensible Personalized Decision Support for Diagnosis","volume":"37","author":"Kuo","year":"2022","journal-title":"J. Arthroplast."},{"key":"ref_19","first-page":"e84055","article-title":"Probability Score for the Diagnosis of Periprosthetic Joint Infection: Development and Validation of a Practical Multi-analyte Machine Learning Model","volume":"17","author":"Parr","year":"2025","journal-title":"Cureus"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115","DOI":"10.2106\/JBJS.20.00749","article-title":"Validation of the alpha defensin lateral flow test for periprosthetic joint infection","volume":"103","author":"Deirmengian","year":"2021","journal-title":"J. Bone Jt. Surg."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1016\/j.arth.2022.06.016","article-title":"Synovial C-Reactive Protein is a Useful Adjunct for Diagnosis of Periprosthetic Joint Infection","volume":"37","author":"Baker","year":"2022","journal-title":"J. Arthroplast."},{"key":"ref_22","unstructured":"Food and Drug Administration (FDA) (2025, November 10). Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests, Available online: https:\/\/www.fda.gov\/media\/71147\/download."},{"key":"ref_23","unstructured":"Gwet, K.L. (2014). Handbook of Inter-Rater Reliability: The Definitive Guide to Measuring the Extent of Agreement Among Raters, Advanced Analytics, LLC.. [4th ed.]."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/01621459.1927.10502953","article-title":"Probable Inference, the Law of Succession, and Statistical Inference","volume":"22","author":"Wilson","year":"1927","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Efron, B., and Tibshirani, R.J. (1993). An Introduction to the Bootstrap, Chapman & Hall\/CRC.","DOI":"10.1007\/978-1-4899-4541-9"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: A novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med. Decis. Mak."},{"key":"ref_27","unstructured":"(2025, December 09). Cost of Joint Aspiration. Available online: https:\/\/cost.sidecarhealth.com\/n\/joint-aspiration-cost."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Okafor, C., Hodgkinson, B., Nghiem, S., Vertullo, C., and Byrnes, J. (2021). Cost of septic and aseptic revision total knee arthroplasty: A systematic review. BMC Musculoskelet. Disord., 22.","DOI":"10.1186\/s12891-021-04597-8"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.arth.2012.02.022","article-title":"Economic burden of periprosthetic joint infection in the United States","volume":"27","author":"Kurtz","year":"2012","journal-title":"J. Arthroplast."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1530\/EOR-23-0044","article-title":"Making the diagnosis in prosthetic joint infection: A European view","volume":"8","author":"McNally","year":"2023","journal-title":"EFORT Open Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2757","DOI":"10.1016\/j.arth.2019.06.050","article-title":"Do Culture Negative Periprosthetic Joint Infections Remain Culture Negative?","volume":"34","author":"Hersh","year":"2019","journal-title":"J. Arthroplast."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/4\/626\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T11:48:34Z","timestamp":1771847314000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/4\/626"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,20]]},"references-count":31,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["diagnostics16040626"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16040626","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,20]]}}}