Nano-X Imaging (NNOX) Accumulated Expenses (2019 - 2026)
Nano-X Imaging (NNOX) recorded Accumulated Expenses of $2.87 million in the quarter ended Jun 30, 2026, down 3.7% from $2.98 million a year earlier and down 13.3% from the prior quarter.
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Analysis
Nano-X Imaging (NNOX) Accumulated Expenses (2019 - 2026) Analysis & Trends
As of Dec 31, 2025, Nano-X Imaging reported Accumulated Expenses of $4.22 million, up 6.5% from the prior year.
- Annual Accumulated Expenses has a five-year compound annual growth rate of 16.9% (years ended Dec 2020 to Dec 2025).
- Across earlier years, Accumulated Expenses came in at $3.97 million in the year ended Dec 31, 2024 (+1.2%), $3.92 million in the year ended Dec 31, 2023 (-68.0%), $12.24 million in the year ended Dec 31, 2022 (+239.0%) and $3.61 million in the year ended Dec 31, 2021 (+87.0%).
- Quarterly Accumulated Expenses has ranged from $2.3 million in the quarter ended Sep 30, 2025 to $2.63 billion in the quarter ended Sep 30, 2022 over the past five years.
- On a year-over-year basis, Accumulated Expenses rose in three of the last eight quarters, with an average decline of 14.5%.
- Peak year-over-year performance for Accumulated Expenses in the last five years was growth of 405.6% in the quarter ended Mar 31, 2023, against a decline of 99.5% in the quarter ended Sep 30, 2023 at the low end.
- Per Business Quant, the preceding three quarters came in at $3.31 million (quarter ended Mar 31, 2026), $4.22 million (quarter ended Dec 31, 2025) and $2.3 million (quarter ended Sep 30, 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 249.71 Bn | 228.86 Bn | 4.88 Bn |
| 2 | Abbott Laboratories | 171.00 Bn | 142.08 Bn | 7.27 Bn |
| 3 | Danaher | 155.79 Bn | 139.61 Bn | 3.61 Bn |
| 4 | Intuitive Surgical | 143.91 Bn | 123.46 Bn | 1.96 Bn |
| 5 | Medtronic | 110.42 Bn | 76.29 Bn | 6.34 Bn |
| 6 | Stryker | 105.71 Bn | 91.82 Bn | 4.50 Bn |
| 7 | Boston Scientific | 63.26 Bn | 58.27 Bn | 3.85 Bn |
| 8 | Edwards Lifesciences | 49.80 Bn | 33.91 Bn | 1.35 Bn |
| 9 | Becton Dickinson | 48.78 Bn | 45.93 Bn | 2.32 Bn |
| 10 | Nano-X Imaging | 48.02 Mn | -131.66 Mn | -43.67 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 2.87 Mn |
| Mar 31, 2026 | 3.31 Mn |
| Dec 31, 2025 | 4.22 Mn |
| Sep 30, 2025 | 2.30 Mn |
| Jun 30, 2025 | 2.98 Mn |
| Mar 31, 2025 | 2.98 Mn |
| Dec 31, 2024 | 3.97 Mn |
| Sep 30, 2024 | 3.30 Mn |
| Jun 30, 2024 | 3.64 Mn |
| Mar 31, 2024 | 3.27 Mn |
| Dec 31, 2023 | 3.92 Mn |
| Sep 30, 2023 | 12.53 Mn |
| Jun 30, 2023 | 12.76 Mn |
| Mar 31, 2023 | 12.31 Mn |
| Dec 31, 2022 | 12.24 Mn |
| Sep 30, 2022 | 2.63 Bn |
| Jun 30, 2022 | 3.04 Mn |
| Mar 31, 2022 | 2.44 Mn |
| Dec 31, 2021 | 3.61 Mn |
| Sep 30, 2021 | 4.29 Mn |
API Access
Nano-X Imaging Accumulated Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=NNOX&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "NNOX", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=NNOX&period=max&api_key=YOUR_API_KEY");
const data = await res.json();