Trabecular bone (TB) texture regions selected on hand and knee X-ray images can be used to detect and predict osteoarthritis (OA). However, the analysis has been impeded by increasing data volume and diversification of data formats. To address this problem, a novel storage platform, called Bone Data Lake (BDL) is proposed for the collection and retention of large numbers of images, TB texture regions and parameters, regardless of their structure, size and source. BDL consists of three components, i.e.: a raw data storage, a processed data storage, and a data reference system. The performance of the BDL was evaluated using 20,000 knee and hand X-ray images of various formats (DICOM, PNG, JPEG, BMP, and compressed TIFF) and sizes (from 0.3 to 66.7 MB). The images were uploaded into BDL and automatically converted into a standardized 8-bit grayscale uncompressed TIFF format. TB regions of interest were then selected on the standardized images, and a data catalog containing metadata information about the regions was constructed. Next, TB texture parameters were calculated for the regions using Variance Orientation Transform (VOT) and Augmented VOT (AVOT) methods and stored in XLSX files. The files were uploaded into BDL, and then transformed into CSV files and cataloged. Results showed that the BDL efficiently transforms images and catalogs bone regions and texture parameters. BDL can serve as the foundation of a reliable, secure and collaborative system for OA detection and prediction based on radiographs and TB texture.
AllenKDThomaLMGolightlyYM.Epidemiology of osteoarthritis. Osteoarthr Cartil2022; 30(2): 184–195.
3.
WolskiMStachowiakGWDempseyAR, et al. Trabecular bone texture detected by plain radiography and variance orientation transform method is different between knees with and without cartilage defects. J Orthop Res2011; 29(8): 1161–1167.
4.
WolskiMThorlundJBStachowiakGW, et al. Early tibial subchondral bone texture changes after arthroscopic partial meniscectomy in knees without radiographic OA: a prospective cohort study. J Orthop Res2020; 38(8): 1819–1825.
5.
PodsiadloPNevittMCWolskiM, et al. Baseline trabecular bone and its relation to incident radiographic knee osteoarthritis and increase in joint space narrowing score: directional fractal signature analysis in the MOST study. Osteoarthr Cartil2016; 24(10): 1736–1744.
6.
KrausVBCollinsJECharlesHC, et al. Predictive validity of radiographic trabecular bone texture in knee osteoarthritis: the osteoarthritis research society international/foundation for the national institutes of health osteoarthritis biomarkers consortium. Arthritis Rheumatol2018; 70(1): 80–87.
7.
Almhdie-ImjabbarANguyenKLToumiH, et al. Prediction of knee osteoarthritis progression using radiological descriptors obtained from bone texture analysis and Siamese neural networks: data from OAI and MOST cohorts. Arthartis Res Ther2022; 24(1): 66–13.
8.
HirvasniemiJGielisWPArbabiS, et al. Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study. Osteoarthr Cartil2019; 27(6): 906–914.
9.
Almhdie-ImjabbarAToumiHHarrarK, et al. Subchondral tibial bone texture of conventional X-rays predicts total knee arthroplasty. Sci Rep2022; 12(1): 8327.
10.
PodsiadloPStachowiakGW.A rig for acquisition of standardized trabecular bone radiographs. Acta Radiol2002; 43(1): 101–103.
11.
PodsiadloPWolskiMStachowiakGW.Automated selection of trabecular bone regions in knee radiographs. Med Phys2008; 35(5): 1870–1883.
12.
WolskiMEnglundMStachowiakG, et al. Automated selection of bone texture regions on hand radiographs: data from the Osteoarthritis Initiative. Proc IMechE, Part H: J Engineering in Medicine2016; 220(12): 1117–1132.
13.
WolskiMPodsiadloPStachowiakGW.Directional fractal signature analysis of trabecular bone: evaluation of different methods to detect early osteoarthritis in knee radiographs. Proc IMechE, Part H: J Engineering in Medicine2009; 223(2): 211–236.
14.
WolskiMPodsiadloPStachowiakGW.Directional fractal signature methods for trabecular bone texture in hand radiographs: data from the Osteoarthritis Initiative. Med Phys2014; 41(8): 081914.
15.
WolskiMPodsiadloPStachowiakGW, et al. Differences in trabecular bone texture between knees with and without radiographic osteoarthritis detected by directional fractal signature method. Osteoarthr Cartil2010; 18(5): 684–690.
16.
MessentEAWardRJTonkinCJ, et al. Differences in trabecular structure between knees with and without osteoarthritis quantified by macro and standard radiography, respectively. Osteoarthr Cartil2006; 14(12): 1302–1305.
17.
MessentEAWardRJTonkinCJ, et al. Tibial cancellous bone changes in patients with knee osteoarthritis. A short-term longitudinal study using fractal signature analysis. Osteoarthr Cartil2005; 13(6): 463–470.
18.
JungWLeeKESuhBJ, et al. Deep learning for osteoarthritis classification in temporomandibular joint. Oral Dis2021; 29(3): 1050–1059.
19.
MuhammadMBYeasinM.Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs. Sci Rep2021; 11(1): 14348.
20.
MalatongYPaleePSinthubuaA, et al. Estimating age from digital radiographic images of lumbar vertebrae in a Thai population using an image analysis technique. Med Sci Law2021; 62(3): 180–187.
21.
AltmanRDGoldGE.Atlas of individual radiographic features in osteoarthritis, revised. Osteoarthr Cartil2007; 15: A1–A56.
22.
AmplodSChoksuchatCSopitpanS. IoT prototype system for healthcare on monitoring of knee osteoarthritis treatment patients. In: IEEE 2nd global conference on life sciences and technologies (LifeTech), Kyoto, Japan, 10–12 March 2020, pp.303–306. New York: IEEE.
23.
HamoodRTiroshMFallachN, et al. Prevalence and incidence of osteoarthritis: a population-based retrospective cohort study. J Clin Med2021; 10(18): 4282.
24.
SummanenMUkkola-VuotiLKurkiS, et al. The burden of hip and knee osteoarthritis in Finnish occupational healthcare. BMC Musculoskelet Disord2021; 22(1): 501.
25.
RenRMaoZLiS, et al. Intelligent visualization system for big multi-source medical data based on data lake. In: Information systems and applications. WISA2021. Lect Notes Comput Science, 2021, vol. 12999, pp.706–717. Cham: Springer.
26.
RenRLiSHouW, et al. MHDP: an efficient data lake platform for medical multi-source heterogeneous data. In: Web information systems and applications. WISA 2021, Lect Notes Comput Science, 2021, vol. 13079, pp.727–738. Cham: Springer.
27.
RenPShengMLiX, et al. MKGB: a medical knowledge graph construction framework based on data lake and active learning. In: Health Information Science. HIS 2021. Lect Notes Comput Science, 2021, vol. 13079, pp.245–253. Cham: Springer.
28.
KarnatiYZapataRMcConnellMJ, et al. ROAMM: A customizable and interactive smartwatch platform for patient-generated health data. In: 13th International Conference on Contemporary Computing. Noida, India, 5–7 August 2021, pp.150–158. ACM.
29.
WolskiMPodsiadloPStachowiakGW, et al. Trabecular bone texture detected by plain radiography is associated with MRI-defined osteophytes in finger joints of women without radiographic osteoarthritis. Osteoarthr Cartil2018; 26(7): 924–928.
KagadisGCKloukinasCMooreK, et al. Cloud computing in medical imaging. Med Phys2013; 40(7): 070901.
32.
PatelPRamoliyaDThumaarM, et al. Advancements in cloud-based solution for medical imaging: a survey. In: International conference on electronics, communication and aerospace technology (ICECA), Coimbatore, India, 2–4 December 2021, pp.811–816. New York: IEEE.
33.
ManojEKavediaSSnehalE, et al. Benefit of cloud computing in medical imaging and scanning. In: Techno science paradigm of energy technology through innovation, Pune, India, 13–14 February 2020, pp.121–128.
WuJXiaYWangX, et al. URP: an integrated research platform for one-stop analysis of medical images. Front Radiol2023; 3: 1–17.
36.
VilaMHAttarRPereanezM, et al. MULTI-X, a state-of-the-art cloud-based ecosystem for biomedical research. In: IEEE Int Conf Bioinform Biomed, Madrid, Spain, 2018. New York: IEEE.
StachowiakGWWolskiMWoloszynskiT, et al. Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis. Biosurf Biotribol2016; 2(4): 162–172.
